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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "9ec1036b-804c-4610-9821-2ee711c1f24b",
+      "metadata": {},
+      "source": [
+        "# Motivation and Big-O\n",
+        "\n",
+        "## Outline\n",
+        "\n",
+        "-   Motivation\n",
+        "\n",
+        "-   Time Complexity: Introduction to Big-O Notation\n",
+        "\n",
+        "-   Average, Best, and Worst Case\n",
+        "\n",
+        "-   Space Complexity\n",
+        "\n",
+        "## About Me\n",
+        "\n",
+        "-   PhD candidate in the Department of Computer Science at UofT\n",
+        "\n",
+        "-   Thesis is on remote patient monitoring using wearables and mobile\n",
+        "    devices\n",
+        "\n",
+        "-   Did BSc from Vancouver (SFU) and MSc from UofT\n",
+        "\n",
+        "-   Co-Founder of a remote patient monitoring startup (Tabiat)\n",
+        "\n",
+        "## Why should a Data Scientist take this Course?\n",
+        "\n",
+        "-   Problem solving. This course provides you with a framework to solve\n",
+        "    coding problems you may encounter in your career.\n",
+        "\n",
+        "-   Efficient programs. We want to write programs that scale well with\n",
+        "    big data.\n",
+        "\n",
+        "-   Interview preparation. Many data science jobs require a technical\n",
+        "    interview, which involes solving algorithms problems.\n",
+        "\n",
+        "## Learning Objectives\n",
+        "\n",
+        "-   Assess options and choices around methods to solve problems and data\n",
+        "    representation methods using Big-O notation.\n",
+        "\n",
+        "-   Develop comfort with recursive functions.\n",
+        "\n",
+        "-   Decide on appropriate methods to represent data for a problem.\n",
+        "\n",
+        "-   Take a client-led problem and translate it into an optimization\n",
+        "    problem.\n",
+        "\n",
+        "-   Identify why code is running slowly and know how to improve its\n",
+        "    performance.\n",
+        "\n",
+        "# Motivating Code Demos\n",
+        "\n",
+        "## What are Algorithms and Data Structures\n",
+        "\n",
+        "-   An **algorithm** is a procedure to solve a problem\n",
+        "\n",
+        "    -   Sort a data observations from smallest to largest\n",
+        "\n",
+        "    -   Find the nearest neighbor to a data point\n",
+        "\n",
+        "    -   How fast is each algorithm?\n",
+        "\n",
+        "-   A **data structure** is a concrete method to store some data.\n",
+        "\n",
+        "    -   A pandas data set is a good way to store observations with many\n",
+        "        features.\n",
+        "\n",
+        "    -   How much space does the data sturcture need? How long does it\n",
+        "        take to access each observation?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "491f7dba",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import timeit\n",
+        "import random"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8b877adb-0aec-4d11-ae70-1dc037c7835b",
+      "metadata": {},
+      "source": [
+        "## Loop Versus Vectorized Operations"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "14abe057",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "size = 10**4\n",
+        "\n",
+        "# Using Python lists\n",
+        "list_a = list(range(size))\n",
+        "list_b = list(range(size))\n",
+        "\n",
+        "# Using NumPy arrays\n",
+        "array_a = np.arange(size)\n",
+        "array_b = np.arange(size)\n",
+        "\n",
+        "# Timing for list addition\n",
+        "list_time = timeit.timeit(lambda: \n",
+        "  [a + b for a, b in zip(list_a, list_b)], number=1)\n",
+        "\n",
+        "# Timing for vectorized array addition\n",
+        "array_time = timeit.timeit(lambda: \n",
+        "  array_a + array_b, number = 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ec6ce099-d7f9-4372-8679-e529c4805094",
+      "metadata": {},
+      "source": [
+        "## Loop Versus Vectorized Operations\n",
+        "\n",
+        "We will learn about what vectorized is in lecture 6."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "8dd27b22",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "List Addition: 0.000406 seconds\n",
+            "Vectorized Addition: 0.000333 seconds"
+          ]
+        }
+      ],
+      "source": [
+        "print(f\"List Addition: {list_time:.6f} seconds\")\n",
+        "print(f\"Vectorized Addition: {array_time:.6f} seconds\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "26a88ebc-1729-48ad-b291-b5f67ecaa0a7",
+      "metadata": {},
+      "source": [
+        "-   Why was the NumPy vectorized operation much faster?\n",
+        "\n",
+        "-   How can we describe how much faster the vectorized operation is?\n",
+        "\n",
+        "-   This is useful in many iterative algorithms, such as gradient\n",
+        "    descent.\n",
+        "\n",
+        "## Search in List Versus Set\n",
+        "\n",
+        "We will learn about searching and sorting in lecture 2."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "9ed3bd97",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Python list\n",
+        "list_time = timeit.timeit(lambda:\n",
+        "  -1 in list_a, number = 1)\n",
+        "  \n",
+        "# Python set\n",
+        "set_a = set(range(size))\n",
+        "set_time = timeit.timeit(lambda:\n",
+        "  -1 in set_a, number = 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "898e5f19-0327-4324-b60e-f3d4268bece9",
+      "metadata": {},
+      "source": [
+        "## Search in List Versus Set"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "794ec0d0",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "List Search: 0.000064 seconds\n",
+            "Set Search: 0.000001 seconds"
+          ]
+        }
+      ],
+      "source": [
+        "print(f\"List Search: {list_time:.6f} seconds\")\n",
+        "print(f\"Set Search: {set_time:.6f} seconds\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e736b913-8733-40f0-8730-43776a61f968",
+      "metadata": {},
+      "source": [
+        "-   Why was the set search much faster?\n",
+        "\n",
+        "-   How can we describe how much faster the vectorized operation is?\n",
+        "\n",
+        "-   What are the pros and cons of choosing each data structure?\n",
+        "\n",
+        "## Selection Sort Versus Python Sort\n",
+        "\n",
+        "For context, selection sort is a naive sorting algorithm, while Python\n",
+        "implements Tim Sort for the default search function."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "34a12fdb",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def selection_sort(arr):\n",
+        "  n = len(arr)\n",
+        "  \n",
+        "  for i in range(n):\n",
+        "    min_index = i\n",
+        "    for j in range(i+1, n):\n",
+        "      if arr[j] < arr[min_index]:\n",
+        "        min_index = j\n",
+        "        \n",
+        "    arr[i], arr[min_index] = arr[min_index], arr[i]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0db17fb4-e029-4b81-b8f0-ce7410f0ed60",
+      "metadata": {},
+      "source": [
+        "## Selection Sort Versus Python Sort"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "58580f39",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "random.shuffle(list_a)\n",
+        "rand_list = list_a.copy()\n",
+        "\n",
+        "sel_time = timeit.timeit(lambda:\n",
+        "  selection_sort(rand_list.copy()), number = 1)\n",
+        "  \n",
+        "py_time = timeit.timeit(lambda:\n",
+        "  sorted(rand_list.copy()), number = 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8329b6a7-a90d-4e7e-a52a-48d3b3d3dd01",
+      "metadata": {},
+      "source": [
+        "## Selection Sort Versus Python Sort"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "4c103fff",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Selection sort: 1.421455 seconds\n",
+            "Tim sort: 0.001110 seconds"
+          ]
+        }
+      ],
+      "source": [
+        "print(f\"Selection sort: {sel_time:.6f} seconds\")\n",
+        "print(f\"Tim sort: {py_time:.6f} seconds\")"
+      ]
+    },
+    {
+      "attachments": {
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+        }
+      },
+      "cell_type": "markdown",
+      "id": "1acc6c51-47f6-49d9-ba4f-79fdc1e069da",
+      "metadata": {},
+      "source": [
+        "-   Why was selection sort much slower than Tim sort (not in detail)?\n",
+        "\n",
+        "-   If we double the size of the list, how much slower will the code be\n",
+        "    in each case?\n",
+        "\n",
+        "# Time Complexity: Introduction to Big-O Notation\n",
+        "\n",
+        "## An example\n",
+        "\n",
+        "-   Imagine you are writing an algorithm to search for a landing\n",
+        "    position for a rocket. You want it to be simple (to avoid bugs) and\n",
+        "    fast (since you only have 10 seconds to find a site). [1]\n",
+        "\n",
+        "-   It takes 1 millisecond to check each element. You decide to test a\n",
+        "    simple search and binary search on 100 elements (more on these\n",
+        "    methods later).\n",
+        "\n",
+        "    -   Simple search takes 100ms. Binary search takes 7ms.\n",
+        "\n",
+        "-   Then you test binary search with 1 billion elements and it takes\n",
+        "    32ms.\n",
+        "\n",
+        "    -   Binary search is about 15 times faster than simple search,\n",
+        "        because simple search took 100 ms with 100 elements, and binary\n",
+        "        search took 7 ms. So simple search will take 30 × 15 = 450ms\n",
+        "        with 1 billion elements.\n",
+        "\n",
+        "-   Since that is within your threshold, you decide to go with simple\n",
+        "    search. **Is this correct?**\n",
+        "\n",
+        "## A practical example\n",
+        "\n",
+        "-   Definitely wrong!!\n",
+        "-   The run time of different algorithms can grow at different rates.\n",
+        "-   Big-O tells us how run time increases as the list size increases.\n",
+        "\n",
+        "### Comparing run times of simple and binary search\n",
+        "\n",
+        "| Elements      | Simple Search | Binary Search |\n",
+        "|---------------|---------------|---------------|\n",
+        "| 100           | 100 ms        | 7 ms          |\n",
+        "| 10,000        | 10 s          | 14 ms         |\n",
+        "| 1,000,000,000 | 11 days       | 32 ms         |\n",
+        "\n",
+        "## Big-O Notation\n",
+        "\n",
+        "-   Big-O tells you how fast an algorithm is in terms of the number of\n",
+        "    operations, $n$.\n",
+        "\n",
+        "-   Simple search needs to take each element, so it will take $n$\n",
+        "    operations. The run time in Big-O notation is $O(n)$.\n",
+        "\n",
+        "-   Binary search needs log $n$ operations, so the run time in Big-O\n",
+        "    notation is $O(\\text{log}n)$\n",
+        "\n",
+        "    -   Note: log in computer science usually refers to log base 2.\n",
+        "\n",
+        "## Big-O is Upper Bound Run Time\n",
+        "\n",
+        "-   Big-O notation is about the *worst-case* scenario.\n",
+        "\n",
+        "    -   If you were conducting linear search through a phone book, even\n",
+        "        if you were looking for Abe Aberdeen, it is still considered\n",
+        "        $O(n)$.\n",
+        "\n",
+        "-   Formally, it characterizes an upper bound on the asymptotic behavior\n",
+        "    of the run time.\n",
+        "\n",
+        "-   For example, the function $7n^3 + 30n^2 - 200n + 9$ has\n",
+        "    highest-order term $7n^3$. The function’s growth rate is $n^3$\n",
+        "    because the function grows no faster than $n^3$. The Big-O is\n",
+        "    $O(n^3)$.\n",
+        "\n",
+        "## Common Big-O Run Times\n",
+        "\n",
+        "Here are seven Big-O run times that you’ll encounter frequently, sorted\n",
+        "from fasted to slowest.\n",
+        "\n",
+        "![](attachment:images/big_o_viz.jpg)\n",
+        "\n",
+        "-   $O(1)$, known as *constant time*. Ex: addition, division\n",
+        "\n",
+        "-   $O(\\text{log}n)$, known as *logarithmic time*. Ex: binary search\n",
+        "\n",
+        "-   $O(n)$, known as *linear time*. Ex: Linear search\n",
+        "\n",
+        "-   $O(n\\text{log}n)$. Ex: Tim Sort\n",
+        "\n",
+        "-   $O(n^2)$, known as *quadratic time*. Ex: Selection sort.\n",
+        "\n",
+        "-   $O(2^n)$, known as *exponential time*. Ex: Naive recursive solution\n",
+        "    for nth Fibonacci number\n",
+        "\n",
+        "-   $O(n!)$, known as *factorial time*. Ex. Traveling salesperson\n",
+        "\n",
+        "## Determining Time Complexity\n",
+        "\n",
+        "Consider the following code. How can you determine the Big-O?\n",
+        "\n",
+        "[1] Example from Grokking Algorithms"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "71eaaf09",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def f(n):\n",
+        "  for i in range(n):\n",
+        "    for j in range(n):\n",
+        "      print(i, j)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "17e02fc8-5ee9-4abe-bf4f-aa5f8b9c7808",
+      "metadata": {},
+      "source": [
+        "## Determining Time Complexity\n",
+        "\n",
+        "-   With “raw” Python code, you can usually count the number of nested\n",
+        "    `for` loops to determine the Big-O\n",
+        "\n",
+        "    -   A loop gives $O(n)$\n",
+        "\n",
+        "    -   A nested loop gives $O(n^2)$\n",
+        "\n",
+        "-   It’s usually not so simple in Data Science because of packages we\n",
+        "    use.\n",
+        "\n",
+        "-   There are many factors affecting the constants in your run time\n",
+        "\n",
+        "    -   How complicated is each step? Is it $n$ or $2000n$?\n",
+        "\n",
+        "    -   How are your algorithms implemented? Is your programming\n",
+        "        language fast? Are your libraries fast?\n",
+        "\n",
+        "-   Implementation issues will be covered later in the course.\n",
+        "\n",
+        "## Big-O with Two Variables\n",
+        "\n",
+        "Consider the following code. What is it’s Big-O?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "0f4dc5ca",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def fun(n,m):\n",
+        "  for i in range(n):\n",
+        "    for j in range(m):\n",
+        "      print(\"Hello\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "182caf57-bfe3-4d25-a27c-13334b0715aa",
+      "metadata": {},
+      "source": [
+        "## Big-O with Two Variables\n",
+        "\n",
+        "-   The time complexity here is $O(nm)$.\n",
+        "\n",
+        "    -   If $n = m$, then $O(n^2)$.\n",
+        "\n",
+        "-   All terms should be combined into one Big-O\n",
+        "\n",
+        "    -   $O(nm)$ is correct and $O(n)O(m)$ is incorrect.\n",
+        "\n",
+        "    -   $O(n + m)$ is correct and $O(n) + O(m)$ is incorrect.\n",
+        "\n",
+        "    -   $O(n^2 + mn + m)$ is written as $O(n^2 + nm)$. We can’t throw\n",
+        "        away either term because we don’t know which term will dominate.\n",
+        "\n",
+        "-   Important to think about this when working with datasets.\n",
+        "\n",
+        "    -   They have $n$ rows and $p$ columns.\n",
+        "\n",
+        "    -   Can you reason how long it will take to fit a decision tree?\n",
+        "\n",
+        "# Best, Average, and Worst Case\n",
+        "\n",
+        "## Best, Average, and Worst Case\n",
+        "\n",
+        "-   Big-O deals with worst case.\n",
+        "\n",
+        "-   If we can develop a notion of an “average input,” then we can devise\n",
+        "    the average case of an algorithm.\n",
+        "\n",
+        "-   Best case is useful to think about the constants in your algorithm.\n",
+        "\n",
+        "    -   $O(\\text{log}n)$ is always faster than $O(n)$ expect with very\n",
+        "        small $n$.\n",
+        "\n",
+        "# Space Complexity\n",
+        "\n",
+        "## What is Space Complexity\n",
+        "\n",
+        "-   Aside from our algorithm taking too long to run, its also an issue\n",
+        "    if you run out of memory.\n",
+        "\n",
+        "    -   Note, memory (RAM), is not the same as disk space.\n",
+        "\n",
+        "    -   The computer will load data into memory from the disk\n",
+        "\n",
+        "-   It will be problematic if you need to load 2 billion observations\n",
+        "    all at once.\n",
+        "\n",
+        "-   We can also analyze space complexity with Big-O notation\n",
+        "\n",
+        "-   Notice that time complexity is usually about the *algorithm*, while\n",
+        "    space complexity is about the *data structure*.\n",
+        "\n",
+        "## Examples\n",
+        "\n",
+        "-   Code that prints `hello {your name}` will have $O(1)$ space.\n",
+        "\n",
+        "-   Code that sums a list of size $n$ has $O(n)$ space.\n",
+        "\n",
+        "-   You have users on Instagram, and you want to store who follows who.\n",
+        "    The answer depends (why?). The worst case space is $O(n^2)$\n",
+        "\n",
+        "# Recommended Problems and References\n",
+        "\n",
+        "## Recommended Problems\n",
+        "\n",
+        "-   Cormen: Chapter 1 exercises\n",
+        "\n",
+        "    -   1.2-1, 1.2-2, 1.2-3\n",
+        "\n",
+        "-   Bhargava: Chapter 1 exercises\n",
+        "\n",
+        "    -   1.3 to 1.5\n",
+        "\n",
+        "-   Additional (for the mathematically inclined)\n",
+        "\n",
+        "    -   In CS, log is usually base 2, but a strong distinction is not\n",
+        "        made because *logs of different bases only differ by a constant\n",
+        "        factor* and constants are dropped in Big-O. Show this is true\n",
+        "\n",
+        "    -   Show that exponents of different bases **do not** differ by a\n",
+        "        constant factor\n",
+        "\n",
+        "## References\n",
+        "\n",
+        "-   Bhargava, A. Y. (2016). *Grokking algorithms: An illustrated guide\n",
+        "    for programmers and other curious people.* Manning. Chapter 1.\n",
+        "\n",
+        "-   Cormen, T. H. (Ed.). (2009). *Introduction to algorithms* (3rd ed).\n",
+        "    MIT Press. Chapter 1 and 3."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": "3"
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/01_slides/2_ds_search_sort.ipynb b/01_slides/2_ds_search_sort.ipynb
new file mode 100644
index 0000000..1d60dd9
--- /dev/null
+++ b/01_slides/2_ds_search_sort.ipynb
@@ -0,0 +1,441 @@
+{
+  "cells": [
+    {
+      "attachments": {
+        "images/insertion_sort.png": {
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wbhDGrjy+evlVbNvKtAz8p594FOwZa6y5Di679ALcc8dtGDZsOD61zfb4+ObbCPGViJ/J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\nRK5CYII=\n"
+        }
+      },
+      "cell_type": "markdown",
+      "id": "8837c9a6-8a69-4356-be21-9298c4317339",
+      "metadata": {},
+      "source": [
+        "# Array-Based Data Structures, Searching, and Sorting\n",
+        "\n",
+        "## Outline\n",
+        "\n",
+        "-   Array Based Data Structures\n",
+        "\n",
+        "    -   Stack, queue, Python List\n",
+        "\n",
+        "-   Searching\n",
+        "\n",
+        "    -   Linear, Binary Search\n",
+        "\n",
+        "-   Sorting\n",
+        "\n",
+        "    -   Selection, Insertion Sort\n",
+        "\n",
+        "-   Hash map, hash table (Python dictionary), hash functions\n",
+        "\n",
+        "# Array Based Data Structures\n",
+        "\n",
+        "## Abstract Data Types Versus Data Structure\n",
+        "\n",
+        "-   Some concepts are generally useful and transcend any programming\n",
+        "    language\n",
+        "\n",
+        "-   An **abstract data type** (ADT) defines some kind of data and\n",
+        "    operations that can be performed on it\n",
+        "\n",
+        "    -   Abstract because there is no mention of *how* data is stored or\n",
+        "        *how* the operations work\n",
+        "\n",
+        "    -   Concerned about “what”\n",
+        "\n",
+        "-   A **data structure** is a concrete method of storing data (and\n",
+        "    therefore its operations).\n",
+        "\n",
+        "    -   For instance, Python List is a data structure because it has a\n",
+        "        specific implementation.\n",
+        "\n",
+        "-   ADTs form a common vocabulary for computer scientists to discuss\n",
+        "    problems. It allows us to focus on the design and worry about\n",
+        "    implementation later.\n",
+        "\n",
+        "## Important ADTs\n",
+        "\n",
+        "-   Set\n",
+        "\n",
+        "    -   Data: a collection of unique elements\n",
+        "\n",
+        "    -   Operations: get size, insert a value (without introducing\n",
+        "        duplicates), remove a specified value, check membership\n",
+        "\n",
+        "-   List\n",
+        "\n",
+        "    -   Data: an ordered sequence of elements\n",
+        "\n",
+        "    -   Operations: access element by index, insert a value at a given\n",
+        "        index, remove a value at a given index\n",
+        "\n",
+        "## Important ADTs [1]\n",
+        "\n",
+        "-   Map\n",
+        "\n",
+        "    -   Data: a collection of key-value pairs, where each key is unique\n",
+        "        and associated with a single value\n",
+        "\n",
+        "    -   Operations: look-up a value for a given key, insert a new\n",
+        "        key-value pair, remove a key-value pair, update the value\n",
+        "        associated with a given key\n",
+        "\n",
+        "-   Iterable\n",
+        "\n",
+        "    -   Data: a collection of values (may or may not be unique)\n",
+        "\n",
+        "    -   Operations: iterate through the elements of the collection one\n",
+        "        at a time.\n",
+        "\n",
+        "## Relation between ADTs and Data Structures\n",
+        "\n",
+        "-   A Python `list` is not a ADT. But it is a natural implementation of\n",
+        "    the List ADT.\n",
+        "\n",
+        "    -   The designers of Python implemented `list` operations\n",
+        "\n",
+        "-   A single ADT can be implemented by many data structures\n",
+        "\n",
+        "    -   You could implement List ADT using a Python `dict`\n",
+        "\n",
+        "    -   We can store the list `[\"DS\", 4, \"Life\"]` like this:\n",
+        "        `{0: \"DS\", 1: 4, 2: \"Life\"}`\n",
+        "\n",
+        "-   A data structure can implement many ADTs\n",
+        "\n",
+        "    -   Practice: how can you implement a set with a Python `list`?\n",
+        "\n",
+        "## Python Lists\n",
+        "\n",
+        "-   Each element has an address in memory. The addresses are ordered by\n",
+        "    index number and adjacent to each other.\n",
+        "\n",
+        "-   Run time for `append` method\n",
+        "\n",
+        "    -   A new address is created and placed at the end of the list\n",
+        "\n",
+        "    -   $O(1)$ time because it doesn’t matter how long the list is\n",
+        "\n",
+        "-   Run time for `insert` method\n",
+        "\n",
+        "    -   The worst case occurs when you insert at the beginning of the\n",
+        "        list because each element in the list has to be shifted down by\n",
+        "        1.\n",
+        "\n",
+        "    -   $O(n)$ time\n",
+        "\n",
+        "-   Run time for `delete` method\n",
+        "\n",
+        "    -   If you remove the first element, all other elements must be\n",
+        "        shifted up by one.\n",
+        "\n",
+        "    -   $O(n)$ time\n",
+        "\n",
+        "## Stack\n",
+        "\n",
+        "-   A stack contains zero or more items\n",
+        "\n",
+        "    -   Items are added at the top of the stack, called *pushing*\n",
+        "\n",
+        "    -   Items are removed from the top of the stack, called *popping*\n",
+        "\n",
+        "-   The first item added to the stack is the last item removed\n",
+        "\n",
+        "    -   We call this “first-in-last-out” (LIFO) behavior\n",
+        "\n",
+        "-   2 minutes: is it faster to use the front or back of a Python list to\n",
+        "    implement a stack? What is the Big-O for stack operations under each\n",
+        "    choice?\n",
+        "\n",
+        "## Queue\n",
+        "\n",
+        "-   A queue contains zero or more items\n",
+        "\n",
+        "    -   Items are added at the rear of the queue, called *enqueue*\n",
+        "\n",
+        "    -   Items are removed from the front of the queue, called *dequeing*\n",
+        "\n",
+        "-   Items come out of the queue in the order they were added\n",
+        "\n",
+        "    -   We call this “first-in-first-out” (FIFO) behavior\n",
+        "\n",
+        "-   2 minutes: is it faster to use the front or back of a Python list to\n",
+        "    implement a queue? What is the Big-O for stack operations under each\n",
+        "    choice?\n",
+        "\n",
+        "# Searching\n",
+        "\n",
+        "## Motivating Example\n",
+        "\n",
+        "-   You want to develop a ML method to search through a video to figure\n",
+        "    out when an bike is stolen.\n",
+        "\n",
+        "-   You could start from the beginning of your video feed and run your\n",
+        "    ML method on each frame until you the bike is not in the frame.\n",
+        "\n",
+        "    -   This would take $O(n)$, probably a long time since you’re using\n",
+        "        ML\n",
+        "\n",
+        "-   What if we started halfway through? If the bike was there, then\n",
+        "    break the remaining video in half and check again. If the bike\n",
+        "    wasn’t there, then break the previous part of the video in half and\n",
+        "    check again.\n",
+        "\n",
+        "    -   This is *binary search*\n",
+        "\n",
+        "## Binary Versus Linear Search\n",
+        "\n",
+        "-   How many steps does binary searching through 100 numbers take?\n",
+        "    10,000?\n",
+        "\n",
+        "    -   We can generalize this as $O(\\text{log}n)$\n",
+        "\n",
+        "-   What is the big-O of linear searching through 100 numbers? 10,000?\n",
+        "\n",
+        "    -   $O(n)$\n",
+        "\n",
+        "-   Notice binary search requires the list to be sorted in advance.\n",
+        "\n",
+        "    -   We implicitly assumed this in the bike theft example (time is\n",
+        "        “sorted”)\n",
+        "\n",
+        "# Sorting\n",
+        "\n",
+        "## Selection Sort\n",
+        "\n",
+        "-   Suppose you want to sort prices of all fruits at a supermarket from\n",
+        "    lowest to highest\n",
+        "\n",
+        "-   You go through the list, find the item with the lowest price then\n",
+        "    place it on top, then find the second lowest price and place it\n",
+        "    second, etc.\n",
+        "\n",
+        "    -   You will end up with a sorted list!\n",
+        "\n",
+        "-   To find the lowest price, you need to traverse the entire list. You\n",
+        "    must do this $n$ times until there are no more items.\n",
+        "\n",
+        "    -   This takes $O(n^2)$ time\n",
+        "\n",
+        "## Insertion Sort\n",
+        "\n",
+        "-   Compare the current item to its predecessor. If the item is smaller\n",
+        "    than its predecessor, compare it to the items before. Move the\n",
+        "    greater items one position up to make space for the swapped item.\n",
+        "\n",
+        "-   You need to traverse the list once for each item in the list, so the\n",
+        "    Big-O is $O(n^2)$.\n",
+        "\n",
+        "![](attachment:images/insertion_sort.png)\n",
+        "\n",
+        "## Live Coding:\n",
+        "\n",
+        "The *h-index* is defined by Wikipedia as the maximum value of $h$ such\n",
+        "that the given researcher has published at least $h$ papers that have\n",
+        "each been cited at least $h$ times.\n",
+        "\n",
+        "Given a list of integers representing a researcher. Each index is their\n",
+        "$ith$ publication and the value at the $ith$ index is the number of\n",
+        "citation. Calculate the h-index of that researcher.\n",
+        "\n",
+        "### Example\n",
+        "\n",
+        "[1] From https://www.teach.cs.toronto.edu/~csc148h/winter/notes/"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "fd88bcec",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# INPUT\n",
+        "lst = [2,2,5,6]\n",
+        "# OUTPUT\n",
+        "2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7c3265c3-d522-4c73-8f61-8ac240af5395",
+      "metadata": {},
+      "source": [
+        "# Hash map, hash table (Python dictionary), hash functions\n",
+        "\n",
+        "## Motivating Example\n",
+        "\n",
+        "-   Recall from the first lecture that searching in a Python set took\n",
+        "    (basically) 0 seconds\n",
+        "\n",
+        "    -   How was this achieved?\n",
+        "\n",
+        "    -   Binary search only has $O(\\text{log}n)$ time, so there must be\n",
+        "        something else\n",
+        "\n",
+        "-   To achieve $O(1)$ time, we need something that immediately knows the\n",
+        "    where/what the item is.\n",
+        "\n",
+        "    -   This is the purpose of *hash functions*\n",
+        "\n",
+        "## Hash Functions\n",
+        "\n",
+        "-   A hash function is a function where you enter a string and it\n",
+        "    returns an integer\n",
+        "\n",
+        "    -   Python objects have hash"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "92fbc341",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "4231697768083451161"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "hash(\"DS 4 Life\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5e90a472-56d3-43ec-ad80-c348ccf52c6d",
+      "metadata": {},
+      "source": [
+        "-   There are two requirements for a hash function\n",
+        "\n",
+        "    -   It needs to be consistent. For instance, if you enter “UofT” and\n",
+        "        get “1827”, then every time you enter “UofT” you should get\n",
+        "        “1827”\n",
+        "\n",
+        "    -   It maps different words to different numbers. Each string has a\n",
+        "        unique hash.\n",
+        "\n",
+        "## Using Hash Functions: Example\n",
+        "\n",
+        "-   Suppose you have a grocery store catalog with prices and barcodes.\n",
+        "    When you scan an item at checkout, you want it to instantly return\n",
+        "    the price.\n",
+        "\n",
+        "-   You can put each barcode into a hash function.\n",
+        "\n",
+        "    -   Let’s say barcode “1234” *hashes* to “1” and “2” hashes to\n",
+        "        “9876”\n",
+        "\n",
+        "    -   We store the price of item “1234” at address “1”. Store the\n",
+        "        price of item “4321” at address “2”\n",
+        "\n",
+        "    -   We say the price at “1” is the *hash value* of “1”\n",
+        "\n",
+        "-   If there are 8 items sold at the store, then the hash function will\n",
+        "    only return integers from 1 to 8\n",
+        "\n",
+        "    -   The size of the hash table is often referred to as its number of\n",
+        "        bins or slots.\n",
+        "\n",
+        "    -   Thus, the hash function depends on the array\n",
+        "\n",
+        "-   This implementation is called a *hash table*\n",
+        "\n",
+        "    -   The hash table is basically a list of lists, and the hash\n",
+        "        function maps an object to its location in the outer list.\n",
+        "\n",
+        "## Python’s Hash Tables: `dict`\n",
+        "\n",
+        "-   You will likely never implement a hash table yourself, most\n",
+        "    languages have an implementation for has tables.\n",
+        "\n",
+        "    -   In Python, this is the `dict` class\n",
+        "\n",
+        "-   Dictionaries have keys and values (barcodes and prices)\n",
+        "\n",
+        "-   Dictionaries have really good performance. Search, insert, or delete\n",
+        "    item are all are $O(1)$ in the average case.\n",
+        "\n",
+        "    -   Average case assumes you have a “good” hash function that avoids\n",
+        "        *collisions*. You can read more about collisions in the\n",
+        "        textbooks.\n",
+        "\n",
+        "    -   The worst case of Python dictionaries for search, insert, and\n",
+        "        delete is $O(n)$.\n",
+        "\n",
+        "-   Recall Python dictionaries don’t allow duplicate keys, that is\n",
+        "    because has hashes must be unique!\n",
+        "\n",
+        "## Python `set`\n",
+        "\n",
+        "-   Recall during the first lecture, we showcased that Python’s set\n",
+        "    search was much faster than list search\n",
+        "\n",
+        "-   This is because Python’s set implements a hash function to store its\n",
+        "    values\n",
+        "\n",
+        "# Recommended Problems and References\n",
+        "\n",
+        "## Recommended Problems\n",
+        "\n",
+        "-   Bhargava: Chapter 5\n",
+        "\n",
+        "    -   5.1 to 5.4\n",
+        "\n",
+        "    -   Read pages 79 to 86 on the use cases of hash functions\n",
+        "\n",
+        "-   Additional\n",
+        "\n",
+        "    -   Give examples of 2 situations to use a queue and 2 situations to\n",
+        "        use a stack\n",
+        "\n",
+        "    -   In Python, code a `stack` class with `is_empty`, `push`, and\n",
+        "        `pop` methods using the end of a Python list as the top of the\n",
+        "        stack. Bonus: Compare the run time of using the start of the\n",
+        "        list versus the end of the list as the top of the stack using\n",
+        "        the `timeit` library!\n",
+        "\n",
+        "    -   In Python, code a `binary_search` function.\n",
+        "\n",
+        "    -   In Python, code a `hash_table` that can hash 4 values.\n",
+        "\n",
+        "## References\n",
+        "\n",
+        "-   Bhargava, A. Y. (2016). *Grokking algorithms: An illustrated guide\n",
+        "    for programmers and other curious people.* Manning. Chapter 5.\n",
+        "\n",
+        "-   Cormen, T. H. (Ed.). (2009). *Introduction to algorithms* (3rd ed).\n",
+        "    MIT Press. Chapter 2, 10, 11.\n",
+        "\n",
+        "-   Horton, D., & Liu, D. (2023, November 19). *CSC148 Lecture Notes*.\n",
+        "    https://www.teach.cs.toronto.edu/~csc148h/winter/notes/"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": "3"
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.7"
+    }
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+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/01_slides/3_recursion.ipynb b/01_slides/3_recursion.ipynb
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@@ -0,0 +1,600 @@
+{
+  "cells": [
+    {
+      "attachments": {
+        "images/box_recursion.png": {
+          "image/png": 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qp069iD/x7rvvbju17Nb67W9/22L6JV8+fRqDfrbddtthm222aTu47OySf8AD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\nImcS37ifyJsv7WVGbup63rRN6qdvC88kGWkLfWh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HPnDdkQTRX9rr3ddXvhAo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\nIyCaMtepBHTxwSSTZKjd2gxcizMm9Wd81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CBeeygAv/71r28nUcnZqQsLU6dQWeXIWF59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qcQzjwzUZ4IMslcMhHw1OHqcEn26ORvLUDmWCFM/h2DO3H/gEbYCPI4dBR3buyVus/eiiKY+M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2qPYbfjD51HTuxldBq/DwztBPUdtOIHsKGsZXaNl+psXPqaLEjhmWN8HfyY+M/7kwMnB346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5Fh/fHt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\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iF/dPyt5k4jWeOk8c4UR2BV4+E3qxJd3NuZ9CEHALCMlJJTjBqP59OpN3jtjPfvazD55v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\nVKGMjsG59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4TP8I6OY3xPH/ST0FiDFBfgxr75MbtkrDfxwRjFIkVXu14V7zyD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RV/vJvk883De3djoMhi/W33jjjd22sH9ehNJ9sUrxBj20psK+nTYbzj46F4qDyE700+vWiJut\nLT7CC7GYxCka6ac42triMmcCNo09KeacOiouxEMvEsSE7II5eC3m0scesJXg44eXC3wyewfuih+c\nFe3u2Xfxlz/lgEdg8TnOg+yKeF+ciWZ+wBy44s20//rDPf5Yh22Do2Q8GvCPLbTv+MC2gUtOrA02\nGSErbDGY8TS81cpc52HL0/HvjU+8EqIEJgH1rJ0BF0DJ1DsUMSoEnjATOMrKORJwyqkmKJSc0gkk\nOohzqOA6CBKq6dwZHPCsbw3BkgDefAbAmrMYlyCCqRBOOB+Vxh713ba2q2jVr7Tn3cfHvXP7Bz+1\nueYc/Z5r89w4+8gJcAiMlrd7ZCRjZM66TvDUd+XJcOAqHtNPOiaZxikKku0TBywh4us/BzwHKAkI\nztOLFno4YdtPe9v+wl5/NiVqmpPM9KzfXI6UvAjkBYgcqDFTXpobzFlPeI2rLRi1m8e5Oej4Cgr9\nDuYcoZ+qSSIls8FQu46CVe0T9sTrce/j3wxwrMOmChB+8pOf7AdJ++FQ5yfYvjxxSKmEM9wuw28d\nN+lxL/BxcJJw87c6BX3wsi79Zs+NEbDwFwKI1ks2BBAOfmyDYFPAIXk136KjOVybP2lZ2/RNXHtW\ng+UiwxImfIk9drl30DWXH4KXn0CTfTJQgIXXgmcXP0QmHNKNza6BQZZaG46CpgLI8MATY/HDngnE\nFM94qQ2ekgK+puNvrW9+49Txp3vzGlOfZ/fB5lcFjviO33CzT/yyoBUvBNMSmxKv9I8+Bof/NRZe\nDrp4AWe2XnBLZ3zFQB7MEbyLGSRTyIwEgnb4WEfQbY6DA3wEm+xPX4lIxIHR+tYR9H92OyDgvzkC\nVTFIcohGcCRKrOkLC4cH7ZKD8JSMIqN9sY8uSQa6w/aIY8g2+si7ce7hXeLU+skJvMiSJJyEhD2z\nx674b30JBjz1lYd7fIK3Yv8khMDAX0k0+yPoN5de6XcwAPeoWEs519+cxvVcHb6e8Ugy8mL7YsSB\noPiv/8jGQad17AG5ogsdxiR80jH8bSyawcNvOofP9tx+kHt7rVTvD8s/9iGZ0BXe1eJJiddnn312\nl69ejsHDOhKvXmpYmy6gka9jM9ESf8LZGtaj3/wlXZHssdf2g0zSGb80sK/sYLiEn1o5gq0NfDIA\nPrzYCXItYaeOZuOMwSv6xC/DB6/JPH0Bj26gDV320UEZXmDBDZxkz324HeG3dw7ce1ZHZzDh7UBM\nX3xlTpfZAPizl34SS3fsvUJX2Tdy5UAuviAPaMQPcOmjvaSfaIJ364bvDmz7JzzU9luswpZKlNNl\nOsTfoxlO5FbyTxIQXmTYuvGb7cBneDn38CN8mH2hn/BEc7V1tZM5L3Q6D5F3sQx7qljDFY4PtqQn\nPjmM20s86JyERmvCEx+8dCLj8QA89MQvNbrB8gLPIR8N4GS/yAOY1sUX/XBpnVdffXXfL/haJ36y\nc+yTP39gb/EJbZLBbD+/CRf84E/68yFiR7qHVxJMfk3lZUW/kjEHfnSMPJBrCRayTWbZDIkaNpcc\ntz/079///vf+IhC9xvqVndiN7LM3xlbQoaxyU/9NrY/o0sZviFt8wcefkk220V5JTEl4xZ/bxpOP\nay+PeE8HvUgh/xLfanJNv/Acr0tukmH6LDbxqwQ1nWJjknPj3dNRuuqDC3+3l19mEyUF6ZSPIMAy\nPrzwIXvXPVsmNvrxj3+851mMZa/E4nSe3eNj1HQSPLjDrS9V2U5tivnGWEdx76qgFd7s0f3tBQxe\niGu84GKTrMtWih/wh47Dgz6TWbDAEFeYy2+Iw8Up8GM7vbRm6/ATHHjyN8WxYIRnuPUMb5e5fvn5\n05/+dLe/8Bdv+jWUXAZczFGCsUuvWxkAAEAASURBVD9s/wRrPmtLv9yzi2gQg/BJ9JN/w0vxB7qN\nkfsSB+CvfudwNb/C/7V29TmcwuU21zc+8bpuHkG0sWpOtOBOxp4wURwCTvgpC0VgLAgGJektBIVz\nOUxw3JTGmHvbQZfSgVPwkpITFEaGADIu1tcHRxe8ws3YBNC9Ei3u9c1nbU9jiUfX5UXj41Xz4icZ\ncFBhINWMdIcGh1FBFcPnUGKvcyTBBc/Vs3Xmfeve1R+eA3PvJjQ65pDEEUiQcGCCBl/x0E01B0z3\njZMoas/m/tm3nFIHAOu07lyze3NcweFkrScg9MWCgIIjSiaC1XNwztXGG8t+uWdz3OcIzXNIFOT7\n+5OSHuyMZKAvpXx16wAbnNatnus+Km5z7mX3K9xoYUftF7wdTvBcksbXhs9tX56gQQl3tWvSvq7b\nmOhbxwsYJJV86SLpyx4L8gSJDkYCNwc+hzljyRZY9lWAVXDR213z7G+BItxae8WtZzgZE67aJ021\nq9ki+8sPCTTJsMShe7aKH4IbHAQ1p+2FocSJywFScgC/0ST5JjEAhvXgjt9o9ixYcllXcMSnCZro\nhL1xYHe5J4dotja/h4/aowUMAaBEikuioH60m9sa4KNRkA+WufEQ7ooxaBHc2gN61X7hEVlyaJWU\noN/oF8Q7uKLP2miEhwOErzoFp/iCDw7EEpa9KLG+4Fhg6ZDel8JwgVMBvK+3JCHFD9olGxzQfV0s\naTP9fTLk0CjRJd7AB36G73GhEf32WBDOppFBsB3Gv/71r+/rmdfBQwKO/uCRwJ9sskFwNI+M4JE9\nI8MSFngnQWMcWuMN3Pu7aXCfBX4SDvwhXpEvuFfYVokMvEI7mSWTEgfwFQPBuaRRe2w+mteiP1mo\nX1vz0NaYORctxqMVnnw3mvkAdtKeSkJ5WRdcc+DowicylEwb01r67BGbqrY35IQuGmOsKzy1hc9s\nb9zE231j4SHZ6M9ROHwVd1jPl33/+Mc/9kOn8ZL+vuw2XoyiTQlna9Fr+kUn6YhEFb2Eu2SawyjZ\npzf4BkZwggUO+ntWR6d7MkZuxcj0GX9Omz2CE103X7s16aCklFpcDTf9eM5nS07RE7T5upQOWxtO\n7b81o3HKQfeNM8YV7vVbT4lOPEpG8YesoAVuZJ/tIf/0HY7gkGXj6Ls56KOHCpngQ9Bkvi+XJF4l\n69CjhEP3cHHucIFtr8irxAcdt//oIA9st/iUjfNCBq/ZbMUYsPHaPoMhMQwndoH8slnZm2Ii81xo\n4FPoLrqtRU7oe3wFiz5LCtMnX2uyAdrRjOeK2prkS8JVbMSGx3dj8Y0tdtHTB5v/Zc/Jh3gOfpV8\ngXMbmTMnv4VmuoMn/lyMNdFhHNmHJxuljp94aW++8pWv7Hrgme0qkY53fKY1wCKPaPCSAh3pJvyM\noQPWcJEfe0ZeyA582Gf7D1e8lJxmw3/5y1/u4/kJCWpxkDXwf5bpR+Ph7L9J9/Fg4jzb0EoP6Jdf\nm0h8452XDHyvevI/OMloz3f1eQ7gNz7T+8k3beSZXosb2SC6ye+Lv+if2IJu8Pd0FSyxBb8r2cef\niDEmbPpOv32t+uabb+72gk6wDZK25rCXcAGvMm043MDwwsKHG848YscKvTDfuGCo4XHa7KSzkbXY\nz2xgc6vn+u7BZ6fEDWh1sc94AjacySZ5BFPsg3Z2Vi23xDaBxY4aa07+gh1wfvOCgS8xjq8QA7A3\nbA8eaK/M++jkw/rTHJK47I/98DELHuOBsXMueLVVt4Y18VO7Cx/opI8CfOjDv1iDT+Hr0Y0msqPd\nOnjCVvJTErD4ISYIh0mXeyXbFj6N9az0vD/c8H9ufOIV/+fGzA1lJDhUDpgjFwwQCApAUSiF8TL4\njAnHSwEEhpSL0hGmggoOUkAvOCEE5gkCJFrBIXxgEnRCZE04EFxXeFaD0VWbWtu55xsub4+MPl4o\n8eMqAI1fx5lvTxxUOQqHb8ZQoCrgYwT1cwK+aHKwOW0GW4LWflbAcc115n3j7uoPz4H2fPJXWwe+\n/gMajkCb/aOjDHxOYOodjILpHtxgz/Y57qhfm/FqMiShIWAmN97esi3Bb9zecI1/wkPd3NYzXWDh\nsCMA8TfCyC0a2SSJmm9961u7DOfcw/8aSz+xIdHQ2jlWh1LJQF9d+ZLBHvmK1JeKLgeOOTce2Ndz\npTH6rdeanq3L9ksMSbz6isJ4gZGDvmBS0oF9F7xIMuKlPRVkdggVNPAZDrn6Jm/Bsw4c59raK9rn\nc7iFrzUd9vkqCUUHuV76aeNfzLe2YE4Cz6E4HAU4ghuHZTga60WjA6Skc8lJgRwY1uPbHJjtAfsm\neAKP7qCngzG8PJvHFupHKznML0YH/tljNlUiwbx4Qi/hDgf8A5eP5SONUxpbogDf2Wu2ml55uy7Y\nM0fCU/BPnvDM4UDi1eUeTeDZV75d0Ozgrc1XYuTutNl3uooH+iRnJSL5A7jhE16iGX99/WauQ7WC\nVkkYcYPDCp7Ngg6+xuEbTvgGHtwE4GIFz/a4ZAbeKQ4mAmcJAngqeCbwdYAhy4JcsOwNHNVkVpJX\nwItf5ATPe9FYzGOuJAHd8Cc4JA3mfsHLWDC8zJHMDA+HHgdi/BcvSQzaA/yyTgcMsY+AXV1pj3te\na+sa0zjPrrU0pvFqsZmvaNhBNhlODiT2FH3xFqzmTbjob49mPx7jhbLKfePgs8rxhG2cEl3um6u2\nf5IKvnqDPx23Lt0nXxKvfs6Otw5n5KIv6SZcfpDs2lsJLHvDX9BNMsfGOny68Igt1A6HaA/H8Ftx\nxQP6TfYd0Oki/MVOdITewl1sTK7vb18L2QeywObQX/aAPElOSbbSRzpCXumN+RV4TPx6BoccK9YH\nUwnvI3r0kVN40Hl2AY50nn7RFXJE7iXc8IhNNZ4dFWt0WRv+cCf39JhOsDdkQUzgxQmbBK61lWSl\nZz6cPsGl5LQ9pM94Aj5c2HnPbCJbqh+frAUXcOCWXtoXa5ElNhS/+Ts2D6/wGd/Q50KDPrW+/Jz9\nJkv2kU+BK10iU/YAHdFiPb6EvfbrAAl+tgwO7CwZ4JPzceDwVdrZanIFpmJ99KntJTr18Tuthw60\nSTSk93AAl3yyb2yUZ3P1kbH+LI25eIdvXgrbA3JrTTLg5Qa9pC8zeWB9ulainI6yN/Dkh8mF+CKZ\nTp7N49vps4RL5w6JV4lguk1H4VlBt3lgz/b6b1KNjpWG2UbWJLq8/PRnUR5syTbyjj8Sr17OsMVg\nmFdZYdZ+V/8vB/CNTK3ylH7Rbx8pSOKxaWRfIpA+sA/0g5708olt5EfYfi+02XP6z7ZYw3rgiDP8\nmQC6yK5KhHrJaB5bNvew/a2NzaYrdPT111/fbUjxGRvA5pATekYv4cSegyt2s5bEsHiMrZslOWot\nfOiePIpfwBPjsSV0Hg3iPf3Wy7bgDxt7b0tIqsWKp83usjNis+w1+Gye+Nx/Ise2oocP9QLGy248\nmiU8tYWfe2uzqRKjbAo7ZE1JV2dScMxl59hs9LDFLvyBM5vIRuMl2C58UMxlm70s8qvBzm5oMd58\ntKnNww82mp6inyywh2izT+aRi7WYu9LYs77bVG5F4tWG2KB14wgkQetAVKBB2Gw8wSug6usgQZhg\nlxLMAjbB5EgFUYSLsHMS1rE+mBw2+JSbUBJ2gk4QMxRTmMA1j5DX3ro9r3TV/zTX53iifS3a8Jch\nKCBkgO3Vg82xextu3+2RYMmXiwIgRsM+MhQVe+Ka68z7xt3VH54DU/5Bi/d0SvDqTw1w/g6XOcG5\nN923/+ragqdu/+o3plJfz+r69dF1BzWJek7OIYkDqhzNr++oBnvCNwaMnCDaHSh8DeBLNTKrzwul\ni4uL/eclvtDxvMrtXO9R8Zpzr7qPBmu4PMPRiyp7JagTwLCbgiIHVIcbSYXmWCM4R046HBrjec71\nLCgSrDl8vbf9bSk2Hh4OsHglIXbaAhR2AF9d4AkYJSHhw9Z7NkbRP9dp/eicffuED/6Z44xpP/GA\nD+JLBHQSAPZX4kRiD072sS+WBDjuBUqCGPg5qKFDHx+nOMz6YkHSCVxwCjj5RMkzh3v3/JTAGTzy\nzC/q49v4Lrii34GcXBmvjezhcYV/Q0+H52jUjwbr46k1rEsejG8cvoRL9MAJXey1fSvxKrmBNgc0\nh3g8kIyigwL5ZIafFvRLQgh06aYDRIdo6wtUJTT9FJV8mtM+w82aklx0u+Q2e+PrL/slgQM2XljX\nXAW/JB0kHxwK4pkx6ESvWIMt43/AA0OfoFniVfICz5vrq13Br69jHSzA0pfc3dsCfokeiQUJWDjw\ne/EDbtbAE8kG9hNM/FTAqdhz+yDxyr7hMdkgm/e3g4JkpoQM2ouXzHElI8YX9wQfLq7sE5zQ4HIf\n/4x3D6fmuG8MOqJdm6R4iUv7JOFIRug/XUBz9JkXDvBwHzx16wZ/rus+OPEqXM2rNCZYtVfrDw96\nIakgceyQW/KSnuKzQytZR5fDqiQT2aJL4Lvwmt6TJ/ZVXEOeyTg9IvcdgE7bvpbkRHt4wyl47sMR\nzvXRXbIKH1/ssBUOd/wOeaVj7I0DPPlyWKZjrUEeyRJc0CrmQpdkItlZ129efFOjifyznfrF5tY1\nX0GT0v65D6659JYMu/AKn63fl7dsCL8OJjvcV2B4+mCLGdHHR5A3dEhe4ztdyrfxa88999x/E69w\nwEMlXNTmSWDTaT6KPpFJsaokBlzstcM7e2/P2wuyaj/giN/sCFzps33huyQA0MI24DNekTcX+tjl\nfBz+4V38o7v0hn10Seriu3WjRZ3Og2MdyRoHbQlj9tMYPGaH+SMXuy2BkY3AF3Dwkt2Cn2f7ZZ/N\nT0fBgyNavGQqqcJW8lnkjezjp7WsgdfmgUt/7Js9BFfi1N7xSdZ0zhOTkM++otVuPjh4QAfsNf1E\nCzzZefJMl+kbvTPPHPjC3x7RZy+D7ZF2542+jqYbirUUc8O9tr3jBv+DHiV+dk/exGz2wk/TxUXk\nCX8kXvnS6Td2IB/A6f6uvpwDeM8u4uO6D/hPd7zk8+sbcl0ykJ7RZfERneJb2U9JWDaILrKfkuT0\nhg6JR+0x++Q/XvXFK/jO1nSQL2PjGgfzVcbhSG/YIev5D1Lza2igw+yNGJHtgIvksGSh/nSZvrOh\nbNQskwfa8xnJpmcXfbeuSwxkDbEu+2GtzqD8CDtgLTjhn9hE/ErX/x9797aiSVItcNxH+fo9vCjm\nzgdQBEHaUdA56SiCiKIg4lkvBBFHkb7wDQSv61HqUXb8cvq/99rBV91V3dXOVHUFZGdmZMSKFeu8\nVuZXbT2wzWefFF77NR5ZF8P66pX9n7iZAw8tGoUj+8h//OpXvzpiMvGX/YoFrY2vxrBvbBf/xSay\n/eJr/puPKCZorc7msP/PVh7F3pGBcOCP7JNvif7oYX8a+rOHFyvnYhv5nOYeA9Y/191PXjT2IZwf\nXOE1pqQsBC7meUbgCBEBpMgMewELhaJEhN1BSThNglWCy5HqJ1QUUaBbENIcQk8ACZg+OBByx56s\nwonQtabz3nq+97/N91NRJ83qnzRzTQY4BsEwJy4owje8l5hxIpJgzsV/UMRIMJ4cQo4eveNT6+ib\n1+4f291QIL5G3+4ZfYmFv/fjjaFgnj7TLbrWOOd9LsxmX9d02rU5HcYGy7PGkqWaJPK0klhfXHCY\nih0C+9ZuzoTT3HPnxnkWPq71O9gnjs+bTQlt/ewNBwsHAU1OcK4/7aD+noF/ly2cwGwda0uIJFp0\nDs/onyRDQYvTZ2fxoRac2dezzo1x31qu9VtT8VBSq0jtrF8iKzBUKBAUWJfcODQ8FZDoZ8tb31yt\ndea9a0fPjOu+cfrgxA+ghUCND+JHJN9k2rXAVPDI7vAh7BY6KcYokAqUBEh8Ejw9dwj4KnjxS4I6\nh+CZboDFn/GBgjCHwBFOEtaKtvokhI70CQ3MBR9dNPwDt723T/2O7qNJvtR8SSo9jub2qt/e+jqg\nIq89CvjZY3uEP/lRBGG36QS/jKeSVwkzHoaj/QgE0RwN6Kd9WJ//9wbf1wJohe7hbc/woksFlnCB\nsxgCnxT3KpCjoznm2zP8FUzIG755bt8Oe4AP/l+tQo4DPmiE1wJvP4/jp+KpfUiIFIj5LDbPeuBq\n1oSnef6uIjqCp1/DKz6ObKCfAoAv2Bz2ozXWtb2TM7jQFTRGa/MU3BRd3YOr7XP1kQNt0hSf8bEi\nkudg0Av0t89g2h+5RMsSJvTDT3sxzroOX6f5akyyAe9eukhq8BatG+tcgiRxw1fr4qVxeJEMx9P4\niqbwURyCCzzg7vncp31p+NO6noNHPh3unckH3+En0w7FMvSHD9x9IXe5kk5JHN72BXJw6TIZUnST\nCItl2RN7YOPMA5+tPS1/Ze98hjWCEe5wdl1/PLRHMmcdhWz4kH+4K7j62gl8sq2fXJETxTo0wO/0\nm5xKliWH9oqecKm1vnt47LiFBx6jp6SW3uCj++ZMOGQlOklS2Q700se2KhooAvCjElE0Imf8rX3g\ng4QbPexFgYGuKS6SF/bI3zD0QsNaYgIvLZzZnHBqj93zh36948A7tGJz/H0+dAUffmxXdg18OkuX\n8YONRwv+VeGWHtEzc8BiC5zxirzrR3O6BSbaWzdZhxt+g4nPXnL7Gpe+T3kmQ2DSt17y4IF7OPNL\nYKIxXwwem0uu9aFbeILDxpMR+MGNTprj5RbfiJ8ONtE4ibzCqEKOsXwqu+rAO3bOXL4sW2G/dAFN\n4GwOGpJZfMJ7MiAXAB9se8B3NGcf6Jaiq7N7zX7JNBmq4Iw+GprxG/AgR2y44iI82RMywneRKTTQ\n8EBrz+7rOx7c03/sp2Y/3btmT/GCvClMy7PZCi/++Hf2Ai/29hDosu/pTd0nT+m6daIfOSWjXph5\nOcCGs63+/wpxFZ0xzxi6JXaSg/lYgN2ht3yAPJsf43voGD6Sef9hmriJjOOnwiv9NW/nKzyTD3ix\n+WwQGH7xw3awL3RZYU+sBQ67xbbQVTpLp+3hySpqGs/ezDbpUX8y2T08wKTr7ARbBh/621r273n2\nl49gcytGowf8orX5fIaPaNgCeJvj5eu777577Mn60SFa6AtGZ7Tgy37xi18cfo0PBos9su9sVzaX\nLdTHPonr2Ct4oiV7J9bCD/DR3j75SzpJNvA/PMiEvYlV0Rou2chyD3LgpYnai7XgxoZr7a9rZ+u2\ndtf6H0p7sIXXlMmZABE0QQpB8LZb0sBwUCJCQlgIogCEI+ScKYkAomBFIAYeI8J4CMII4BQcQmI9\n8xkcSk7AjKFYggNnaxHomue1eQ2eNvsa9zafp5HeaRPNolvPGWaBssSMIRZwCZ4YPT+rYJQYcImm\nJE6yj3/WAgNc566j/1yvvsfz61Mgvu30lVQIpv30oZ8wCtjoOIfToU+w2/zg4aeDrpfslXgYTy/p\nZ7xuJxOOawdbIYjgUHwpzXlx7jVjJpwpt42Z53Dc57m3L8mHoMPeBUU5UHLKuXGAghmJtgQLfpo9\nTdjgOd5ky761ZzRl++idAz6SNHbVebZw1fciPI1zzP3UZ64gSHDjzxv4z2U0AWQ/PUKzaNSa4Wvs\nhDXX8GzfX3h4pnVv3/jExyjS8DcCN7yUuJFlvkggBqaEs2RZUaKEFD8FSc7RjG/BewcZtiYYklWB\nkqIBH2dP7JmAynO48EUO1/DDGwEW/YKn6/bYXtBKsMa/xU9ySW+0xh83z/+ZdINHOgemBqb9SFxP\nqyjkDD648ICfefrMhSdfjnZ8MTzMl7R68SBJQ6O5jrWMQy9ntBYLKApdrsKCs31bxx7sx5yKCIoU\nJdFkFw4S+/w5+pmXrIIDJ35GIiKOgI+9KF5UfMAbsMw1R+AqEaHDCllsSzgrVigY+dKQ7ruvRWP2\nR7HHT5zhqx9stBSr2LPCENlT7BcHkcV40Tl4YhgFAQUgcmm+RMsBpmZO8yY+PZt99oIu5FqhzH71\nwZG8oQ2eorFGXgXpFXasI6Ei3/A3R0O7i/WyVOFVUdKcq5XAK1BLFBQKo5ex9oJWCitkTqzHVigq\nK36Bz38E25mcGqfgY04JJTwUvMgV/NDOGs7u7c2Z/FqXHtqP6/iOJng9vzzxDB7sl/gEr9iub33r\nW8de4aGRKXjbJ1mWgJFLMMUzfILivwSM/SDTyYX58RqOe9NHPvFDzKugS1fIH16xn3BySLzonXGe\nw0VibhydhgNdoBPsGzrwlegarayPdtrEx3MNjekp3QfbGU8uFu8ljfYc7dGvOcm/2N8LS8V4Okj2\n/OrCiwUxn2u40lXyIL7wUoYc2QfY8EZTPwmlq/ZMj/35H3+/nG5Z2xd6dFERgv0IF+f2aF++HvvX\nv/51FNfth96a688GkU80gk/0wG/2j9zhu8P6mv3Dh89AY9f5DHDoG/6TxegEbrTSF8/tA52era+c\nyB4bB1+4wJEMK4rhJ5p4ln9DbzaU7Iir2Rh6DQYeWgcMOJ2WLnnxSvZdk+vsvxzL16j8GDpp5MU4\nBc54Bj75N8Zcss7GoBUe0ml42Bs8wbAPeKCHNeUHigJiJ9fsE/sDX/uBP19NttkTBXPw4aJQqyjo\nTIbYButYz56NY3/70ANdwUJ3Mige8fU0PYKLeRoYxoQ3fO97a2/tr3t7RSdy7etIOsGO4YfCtMP1\nORqY+9huRoFJ73ndbDygc2JmBXB+SlzFJinksTHm0W1+z1jxAL0g3+Sd7siz6afCHP9B5tlS8S/b\nIV5TZOylLn2s7fZIP7vPDsELLL6f7WGL5V/sR3YWbnQ2m0XH6TLbUEtm7MXhfvY1zrNkDlwyaT/l\nnOhlLfGFAjRaiL3ZP7rNL9JvsQl7mS0Hgz/1p6PsiX1il9DFf4zlV0/wtb4Gt3kdrp5Zi0/wH4/5\nUpk99NxaztkQY8HocG9NsYdYwa/GijvlFK1pvtjPC2Dw7bP9KaD70wh8lbhLP1qQBbkq+eA7+aGL\n5afFZ8byGdaebeIVzfXNvc7x9/X6wRReMQCDtJ1JhIZQetPoixHJi4BWw1wHAaCYDILAiyAyGAwI\nZeZ8OU7zJMmCEteUR7OmI1gUzjWFZ7jAhwfjYQ4FJqD6zrXgeXbdmHPz3oa+6By/7blrz2aLdvol\nj4qqFB9f8ZOz8KWBLxrwhhF/un4exggJChk+c1N+565bZ1+z/sfz61HgOp5yfBwWJyDJpEcMvoOz\nkJhLRAS2iiNaPHLGU2M5AomIdThxgbVki27qo7/O4dFuwChot54ER9AtcPaVBBtijHmdgwHmdW1f\ny1wHGXawYfbEkQlArlZhQRDDntgTuyWBUXjiPCUes/gSDsF1ftMN3pOOcJgBVjh1biy8boKfeY72\nFJz2JZiRmPo62iFYotsKW++sv/nExr9Ks6/WND8cguXeGLIpMMM7STV5hY/il8SfrEUjPkIwIrlQ\nFBKwScL4H3LqkNDhdTQ0V9txIScCQAmnIIicgqVIoaEDvOgSfaEnaOVesFrjx9zbB96QJ/jwlfok\n2nTGfD5Si2/zDF+HPYLpDJbDnugin+tsf+AJHvlYR3hF5+jbGnCig5IzhQ4JdLLfGLjZA11nPypM\nKxRFY2vbb/viIwTV+AQOmOEOB2PpH1pWqDPOXvl9tuG0EnP7wgNrS1Ykl/iCf+A4jJe0+zIA7xW1\n6DSeeU7XFeEUIRSdyI/+Gp7Y/9Plv3z1XoEDf9kKexa7kEV91nePzslR8OwBrRRwJAzsmkZuJT2K\nGZrxc44+c2f/1Gm0I8foQvfYSnQxB40VxOEnUNfsHQ5TduGuIGM/0dB8hT1/YkESRo/QWIFE3EcP\nwNaMLTG8WIkAOsNB0UZs6Mt48R362UdyL2lSWFGUwU84KuThhbXyG+3fWuamq/kJsim2SH/IVzLl\nHO3IFVpIeOgzmSQbYhhxij3gG7x9aSm2JVv0y1cr7JtkGc7wBRtuDs06XR8dz/+xb3Cd7ankmr4o\nupIX/FBElViLqyTA9Ng8+ore6OJaoq4oqlB2WrpA16KJNTR0mnQLLzjWjGWj0AN8B9sj4fZC3U8z\nwdbMg4uYDt/JOZlVwGYXrUdGyLV4kM3gL8kBvsDbODEGec/+mWOsgqqiK3sBL4mor4H8bBQ/8UAy\n6gUIXtHt2cIPHfDPL1j4J7YIvT788MPjKzM6Yg3jHexMMmc/dICMsEvkmO9Aa/wgA+Kc+G59+9bQ\nRgNznu0T/ujlBY9fipBtDa72hQZobf9kgH6yg2iGDper4G4+G65fa118BYc8whUMsMhF8bb18Yxu\now2fye7YJ1rgNx9O9uyT3VV09dN09PCc3Ctiwhf/FNv5X7yyd3jACS7opnhAhthd9PcMbYzne+gf\n2+Agd3AEg04rMJnPZ6ONNc21jrnwxlc2C13Mtyf2Awx7srYvnNEDjPjiOTjuw/kg6D3+Z+5t3wZ6\nkW9/TsfB/rB5CkLiWvKGZ1pwXKPNY7sZBZIlo6PhpB954/98oaooKAaSTyiS0jt2kow3h02mH15O\n8UHsEr0n13Sbv2SfFOJ86MSGsUtgKi7iK3/CToUTHDS8Dl/zrlYc42/xW0u/QrDCLRkRV8CpPZkf\njq41zzro+BzfPH37PPhkw87po7niErE9P0B22VL94jJ7fP/99w9cox27xS6ws/4EA5qbY098vF+2\niI9aN5wmnl0783W+Bva3kdlNfeZk//kgPLEu++V5hzqVYihbJoaUR+qrgUM3xVL25s8OiE/4y+98\n5zuHr2I7i7nRiG8Wr6qtKMCyffguLiFL4ga2Duxw7dp81+09PB7K+cEUXjEOs2opVY6T4xbYcMAC\nN+ONodgctWCFURfoEAZKTGHApFAUhOHocM8QFFgkJISccDtb23UH3DgS8Jw918zVwt89vFJQYz2D\n80Nv0cI+z+3X8+jjecccP2GgW3MkIIJhhlqwKLBjEBhyxSxNkOyLEg7G1wLx4Hi4/mm9uca8btzj\n+fUpEP93+nIA9NkbcY6csZfMcvACYbrnuWCc4Vfs0MARZEvQOTcBBEcnIKfXggfFDI7J2NadeLAZ\n4LMNDs6JvZCUky3FH0mFucme+RPedZRJtho/nY5EhN1yCOQ5TgdaeMbJcbwST4kh53layUwJX7Cj\nwzxfh8+r9merCpCDA4e91Tfp0/6Nndf73J4Hw/2EAw/FCnz1tatAUrIjuZHkCADQKD6ZC1Zruq9d\n17/v1X0+R8GCbJFRCaFgSNGCPec3WhedBCR8kGCEfJJTQRf5EtxESzjl25q/4wlnyYvCAbumYGeO\nxJbskhs64eDDBFBkSAANd3LN9klArU+/knu+zHNwyBvYAqqKd3BxwNdYe2BrnR0KaeaiAT1DBwf4\nbC19FOiDR87xD75aPDhunv9jHvyscVq8fLK+dOLD+XJ+XeESbeFivv3xv2SCjVAsQF9BJ9uBpvok\nxgqckmR0MVdMUDHDOLjBteKsPvgYiwb2iYb2TS/hgOZe8NgbGmjmaPYPd8UERWh2hY1yjR/orbDh\nZaECGPmyZs069N4LILKNrvaCP5J+sg8H8OBmvv0pNOK/Bm/4w4nt4A8VmdCHrJAnuiTBwr/2ap7m\n3rG3CRe98cT+yKR17D29QU80J0PkBQ/xt4IIm8dO0y37IzOa5/CU7KGFPRnjSO+Mgx+YitNiAXwH\nA10V5uirNTR4o5dxaKq4UuygsFjx/molhOhRa7/oDJeLVeBll8kl/SE7ZAD+9M94B/mgUw42fcoX\n2Kcl42wEf0c3Z9JrLplX4DTGYW8VOsHXyAwa4HF46kNv/fZufQU3doTM8Y/wxDN+1j6c6Zs+e0r2\nyYnYCgyw4KlIbNy0ZQcy6x98Dxd9cIBPNHFvb3juhSMZxFN2gb6w53iDzugNHlzJPdzpCjm3Hzpk\nn4rzCnNiwvRNvzXF9pereOhXJWIIeqowwD4r9pEF89gdjZyRAwm3r17hS659EetrcTEB2PaoxQfj\nXLMz/od7ySx6sTMls/htnewWPohp2Ceyo8Gt+Mde3GdvouEx8Pk/c300JEPJmTNfpXiiiEAG4wW4\nZArdyBie4ieaojX9QTf7QbP4aFlr4o3kXJEEHDQknwr2Gvuk+MBPinPwgZ7gPRqghTirl1Lkid6y\n5f1Hq3DhP9lBump/vtrDFzBr7I217cMXZuJ9tINjfCJDcJEzKh6gPV7jBXm3ji/Z2EiFCrYqets/\n/TafDpFdzTx8I8PwRiP2RDyiCAVn8WOyYY7r7uOd/vvYXrQXz8giXX22vrL2dSR+sJ1og0/4P2FE\ng/tOl/bx3zgnS9aa13NtL034eIVXsYN4VPGQ7eNj2HQ2jS5ofB97zyf6IlIxUZyFL3SNTtFVMYk1\n2Tg65wMILy3oAF7Hx3N48StwUceRt1ufr2eP2JPyLviYr70InudzHdcd0x/t49zvzTy2kw0ku2x5\nftbexBr+Zj+bB6cO9p69/eUvf3nEYeyXeENh0nh2ne1rH9YNR9fBsRab48tZhVf2G73RlK9GI/aN\nrUVHsZ+jl2PiMTZM/MTPsfVse2s5W4tNtsZ8Icd2+ULXS0x8zX5aH3x2Ti7CN/DbcKLLdFrxXWzb\nOu2ne7Bct9fj4gH8c+8LrzFoCuZkUswniJyvt5+CZOM5QYacwHCihJySEM4SAcZD0M6BlkAL4AgU\no5NQWJMhMI/ANj9cEkaGh4I4p5jhHixzKRs8rMEZ2YfxD71FC/uMHnPPnjsob/RLBhq/w+gef73R\nYaQF6RJgX8RwEgw6vjEEFV4ZcryYcFsrmHCb1xPXx+vXo8Cke5D0CYglBb7ycRbwchiCaPpMdyQm\ngnFfKFRUwEv8ZfTpu+Sbo+MsHYIGAXJ6Nvnqmn47yIkkn34qGpAT63IkHCtHosHVvHP7aD/zbFxj\n9c/1BfGcFvwkE+TfwTYI5CUsxsBHEiqYESC5ny2cZt9dX7eH8J/0rK81z401Zh/X+P1s/oQx5+mv\nyObPiQjYJJISv76Mk7hr+7x5v6857+fa9imowRN8kvRLQiVeElP9BarRhKyWOJNfBztFpgS3eJzv\nsNbEy72j5+GlT4FCoEOmBcTkVjHAWPogSEQbsiSxpVN8knGCJzixkeaQcwU7uDhnE80ne3wj+9ne\n4GiM+QJ2AZ8k1574XHtHC0m1dekgHNAObvwtPrk/5/fAd8DHOoJG+sgOOKxxWkm6ANILFtfG2Dva\nwBvOdMb++Xzj7FUCQc8UI/kF/DMGLwSm9oM+cMdjuKIlnx5P8QF+1kMvRRA46QMruhsPH/2a/aCZ\nr14VFeCMbtYVjFtDkH65+MpvzaIIOOawbb4OYt/Qz17InniFrD1ZhTK2il2owMRO2osWLs7gKeL6\n+gIP7VnRx3gH+O25efAIzuyb/dOGxi90MoYsoA/YxnlObthU1/htDFuHf+hvnrXQD85ohnZkiuw7\nXBev4QuYFyuhF/vxA2QfTSV04JID48i+woov6BTd2FQNXRXbFGTQRKGxBh9HOmcNCab5Cun4CG/r\nXK1YlA6gI1mXiHlGr+xRjEJmPE837Q894KdYZbyzfomxJIs/JKslyPDRJk/Cd57hRha8lEAPRUsF\nK7Q7LT1SGFJI7CUnneEP4RJs+0in0RXP8I782UMtOpnn2jkY9ts1nNBCAkcf6QBZREu0Zc8ll/Aw\nB6/R1VhfRily9Sc9yD274CWpgi0a2QMc4WBdtrMXdewE+0CfjLeWvbMBxto3+ZJX+OJSggkOmfLF\nkj8/pDgQfczR3KOpfTSXzUE7BU0vOxRu+Sf7MpYO44dYhfyhMTlQiMRvek3+7REd0HrS0brRVD+5\ngztaoQ++x3s2gyw23wsnvBc/iZ/ZA/ykp+RPsYxdoj/uzUMHDR7sM79mLjqiiX2GK1zkVeCI6eBU\nkdNcfDIH7dnD5J9+GK9Ajs/4CE96wI7aj+KR4qm92j+80YyvhYuYjV2k6zX7ggP58bcurYHeGpzJ\nmxhLPmFf5HvymD4q1NqPMxnmU/GHrWAzyCf9IMd4zW6TTbZrNnR0wD3+zefnrqP9uWf13RRW4+/i\nPPGyfvsCm8zwaWgjtpev85sKfn3BjE9zTjh9Fntp7ft2Pke/9tAzui93UmQjv3SVX3HQHfavlo3I\nd/CLPooRe4ITTONdO/CV7RaHKzLSAbpbm3Pqo39shD9Dwe/SHfYATuwS3zxhJF/pZXDAhnNtPvfs\nXEu+zj33DC78OVvui1AybCx86LQ9+qqd/IpTzGkee+k/DOvPMIgX2S8v38QM7uEY3uHSOZzQ38cl\niqJ8N5/Bf/QVKxuK7nBlnyq8msfHiImMEWuyZ+x2sJ3hwAd5keXv0rJf+tHe36SVd4pB4JVMWIu9\nE0/wb76YtV90YO8c7CeaaK0Hhuv26Nm8dn+f270vvCL+ZNZ+L5DpLa6ARdDGoXOejAnFFbCcVlDJ\nmUsABN0CAckhZ02hJM8KryXQ4E7ltS7BNN9R0GMc/BrrTCH0hbe5hMocikroGTZBswBAUOQcDOMf\napvKNenTfj13UFT0iI6dGzfPxnsu8ClpEMzhr2ANXyV7AjKBmL+t5W2cgDuDEF6t07115vVc9/H6\n9SiA1lr07Z4zZ8h9YYJ/eCmI5qw4OQE0PeeEOAkFBnPpFUfvKwXBeTKgwCLI4Kw4idaZ2NNtB3mg\n3+ZyiOyIYonCDQcnAZLIzUZO7aF9zGfz2rodc7w+AalkXKLGTlgbLpynRJEMS8b0+drDW8gKLHON\neQ3uy3Ca429zDbYG/r7/+WxeG9+9a+1l+BnfnGg27+l1ibQ38XyB5E8h5GIVXiSU6HmTFtzwci+w\nEBCyJZJAQQU+KVZYV6AoyKl4wr6THbaFv6lQJJGVKD5ZAQn5qUhhT9ZxRAvXZFGb/UfH8z7BsmRU\ncAQvvoS/I7twJU9kSdBlD/mp/E/FLmeJoIDNs/waf4SWklMFEUc+yhp04rR8qn3xsehML/k+69Mz\nsgsP/lgffFw7w4ncOGrx197hAh6dhptmL3y3vfCfdFLRjC1PF8CAp3XJBljG4gWY1mZTJH3sAt+P\nHwovbAsfgt/4KxlXcHY/8Ywn4Yse9NLeO9qbMfHTGLiwIdZxoJ/A1HW4+YWG5IZctZYzm6A4wbYJ\nuBWg0NmZzOGDIgNe2Ct74YWEwoJ9wimc7VmRSUFAMRd9xEIKS74EZC+T6fjjjJ5wqbW/zvpdG0eW\n0Ca58gx98BEPjSG35A/+yaB1yR58xGng1SQQaEh/jMNjMgEuvKIXu40eDvCt6UVJBS37l8iRIfKD\nJ5IKPLKuF3uKTOiR7MMBLg5yiXd4QQYr8JpP1sV0ZIj+4CsewZNswlmCg7/0wT19sAfz7RHNNP3o\nRDYritkTu0I/PINPPHGNz/rrA0eftcizYpEEiXwreNqLQpM9oIPCF/uFhvjXGuBo4MI1m+I5vJ1v\n0uBiX+hh72QYrfh8/EErtoxOwoms4zl+02tj6TAdwVN9ZO207BH9kMyiFX7SmWgJN3Ig1pdAm+8e\nPcUXaEB3yKK9pC9yAvbi2frSyVwN7/2ZAX8b0TW6a2jjAJf9Q+N+OstvgC0G5b99lUvv4Y6W5Ibc\noQMZ1I8O8GLfyGz8OBZ7/g884wf5Ilfm56fgDxd+whqea3QejfCbLrBDEnO2Hc2S43IjMQj5wTt7\nhAs5NI/dcYCjyBKu6EDG6AF/SZfggu/WIMPG2x84/CN9RU9j+FvFcfPEhehFZ/GM3CqAKBLQVc/Z\nEPRVNJULkB37zIegDzrw24rcl6uY7KzffsgQXWMbK/ZYBy/Q2Tjyhg54ZU/wJDdsAfzQnf1U3IcT\nmvoPfeEkBiADteTFvT0nRz2/7mzey9pNYb0Mzm2eh1dru++ajOI92/Ns6ZL4Bb38KRAFGjpIFuac\n1g5G94/n21EgmmbT6CU+KAaWY4mbFTn9iRB80cxzdG0e2yLuxD85ldiCLY9HxtMleioO93KXDmSH\ng2e86850hZ4rynuRwl8pEoJDJ+kOe2G8fbDRfCVfJY4VS1gj2w3ncOp8bGT9Ew7dO5NP+2Df2ON8\nFH/EZoq5xQN+NcMGsClw4v/92oW/Yves33rwZB/8SRu/emBLPVfc9vJNvuolHnppkx7B0A8OfOS8\nCtN8OFqIXfgStpeda+/Gwpu9si/91uAP4c1f7816xvONbCr7BR821H++hpd8UbgaDy9+mQ9nD73I\nUofDS7EEm8cf4yNezT3N9a/rn2Pu0/WDKLzuBE9pMItQCc4ZdE6ashIGQimAINQcJ0FjNAQTHKPD\nHG9MBckCAwaE4AW/s3VcE14O2KHpc1jPMftcN97aEgWBLMEXRMKPkRCAz2LvAeSB/xM922ZK1xnd\nHHgbjZ331nj9nlNsCs4QozfDI0BCY/xBfwGVwitjYFzOAIzwAivYnT1/bHdLgXgajbvHN4VVX5j4\nkp3+CqYlKhVUJV++cPTGVvKlcb5++udrF+Pwlp5zIJIfwYIkuPXMaU19jnSW/HDk9JVMWd9beQE/\n/Z2NbDV/9u/XreU8x5NzztnB4bc2XMivBNEbYG+YNW9XfW0DFzhe11rPWm+itY8d9lx3Xs9x1/XP\nMa6Nm2PtpXvP0azCqyI8+y8Y8XWyQr2ECB2bs9NCf32NAVcAxm7wEXyD5E+Bn7/gZ5w9xy8NzwSr\nEkcJpGRWUioQc/BFAiOyDJ/WNNe64REO4TzHGasZAx8yLcgpQK1wSJ4KuuyDfOrrMJ8P44PgJtEl\n5/yUZ3wgP1nCTNf4SOsI6NhRflVAJqC2X7YUrgpm/Gg+VYAKFj7xv+a3x/ZiniPf6iw4hB+80MJ+\nrA+WfRiPnmyC4FXxkP7rL2gG31x2wJrmC+zpEtopcHgm8fPVjaBSY3sEkBITSUY8jidghbMzv4N2\nAlBje15MAKa5AlaJJVsi8FakIC/6yIWCgHW9QBDgi2s0a2jWkLRXdEMHdCLjigzg4gVY6KxQoogq\nUCez8EFbPJckoB26WZ+ckCnrK9ZKMnZewWGngT771d91+3cf7l27h0dj7AkPyJSze+sqdpc0mNN4\n8mD/1iNbAvz40/rOnhtLTtlrMI21Ri/kJZj4cFrFFmP5BvaWPSEbkhvX+uEMDzys4KooWLEJX7Lh\nFb7YDXZCUqKP/JMV/LFP+9NvDDnz3D7Dn03BW3r9zvraBM/Ii/3Eh+h+THr+D1x73r7Jlj3xgRJt\n+4QDeQHfPtBCIQ5+6BTNJ+x5Ha764vN1ffHdHtGJbUEX/pm9QCs48mdsir1WDEczeq9YRqb5QQVN\nMuoZGvGF/f1TesweeRZenck520THrU3u2GoHG43mWvx2Fl/QI195XS08wRJf+ArIT0ytXzOevIjp\n2WZJLFz1sU/m4aUiEzli48Cjy+QTTmSB/MPNPvCEzsZraxhvLw5yZC7baz66sNnkyj09QXdzrEf+\n2R78VjiAB1tKrvAPb8gHe0D+FcPxCA/oGhvCZyiI2I8DzdGB7GjwAQcdzAcLv+FgnewYmtBnhz72\nDK58q/2w2emfMfBGD+vbuy/z/d1DcqGRZ0VORQL+CT/tCY3YQLjYE54oFKCT/YCbDJEjdGEX6Tt6\nWwseaEp27AdN7FN8qGDFBtubPZMXHw6YgzbiNcUZtLbHGtzA1/A3Oe15Z+N65vombR/f/U3mvsoY\neE08rTdxJTt0T8FObE+P8UvRVYGGvpNzc7Jf4fGmcW+dh3iOB5MfeCG3kkPJL4yhxxfrYwW2iS7T\nC/3mTRmlw2wBHfKCQcxAl+htzRw2lR4q2nmxUsFu4gNuvGXz6Ce9UWBkL+DALoit4cQmko3msWfs\nGN2zBhvB7mvh3rX7bGg4eGY/7AObw9442x/dhRMfzZaJ/9lFdoT9YS/6wt81W8Zvth+wNbaD3/3d\n73530Mpa7K180q9v+V/xh9bc9tc9HMFRKGdb0ElsI4aT74oL2Drz7A3dtO71udbA3I/6reNlFh48\nWy9HxA/oqbjLrsqt2LjwMg9ssS+/5QWYl5Rkg53z8opuoxP/KuaYtDdfm/A+7bnf/z7IwutkSUzE\neEJCuAh/QZe+CrOURvAm+eI0JdWCEkkdQwRWAtC5teaz+qzVuPDwTJ+DoRGIUEiKRunDzZkiUWp4\nzSQ++Pf5HF3sYdLGfc/0owlldDAWnlF+fMOTjIV5ezPWAU5rBM/ZXHB6xmhzKgqvAiVBLR71HKzW\nC/a+5uP93VFg0n1C5ey8GfzHP/5xJL+cEsPNyUjI6JQA2FtRhdcCB86QgxDICZ7JADiKLJfrywa6\nLziYvIXDxMO152SRTHLsHLqvTAURrsmR1tg5f+7jptfkPfsDZ2vnOCUG/haTAEkiBzeFpvfee+8I\nVEt0brrWfRuHttEX7jvvBEgSKcV1BSv2lG6j0cUKIiVP5mSrXU94Ez4+OErSJFi+hlGoEEjoB8cc\nfGLDHYoF5ELgKgAUhAmKyI7n2bV4ehMehJd19gZHuJELXxzAS3KniCYZtq559kqujGcHyT56CSIV\nOTyHu8CWHMG1pFcAKvE1tgRYkuk53OhASTMa01F6qzChUCH5xIvsr7U69j3p1zf9gHu2mR9Hc+sK\nhO012qCLffuJl68q8ACNJ6/tXWxgPxI9SR9bAD90UqwUUAoQBe2CR3RVZMFzc60X7mBr4YtuFUXs\nF472XJtzjVOsU+Tlh56sYJnNskfj8IQM+0JV8GvP1g1GZ31ohf6SVS8YyLyv/QS7cMQ3BWZfWvhC\nAyz8tUcyar/k1T2+ogf6kCdJVHxrH86tf92151q0cl/f8WD7x7jZum/eDoeskm/FC/uHpwJIPAKr\n9fDHYQ4/j08KJBJ9NOMf0F+yYr4kz/4lDoo4/Au5jw7kCixz+JeLZVvQG0/Fj3jPVosnyYB4Dj8l\naWhLrjTFIEVOxS+wraMg6hAP4h286aS99jWNQpC18HC2aKYvupF5eg+ePVSwJPt00xqSYrJPdtgr\nckAfbmOjWtM5POCwX4cPOqEJntEtvlkyKz7Hh9MqgMOJfOIT3UAjhVo2GP5eSCgsWgeNvEj3Ep0s\nK57ZB32aOLlOLlyzIWA6G2sdtgbdJ+7RE64KAfRS4cgYfPHFkpc2igLGoiveo7GCq78DyE6zlYoD\n9maeGIZM6o/e5uMZGiXPcDMm3MBvjHXYNHJH1sgdOaMT8LU/DXzz8VYOwl6SfQca81NsBjqzA2TF\nCye2g0zSC2vac3DMYcfshR3DN3JpHF7DDS5sGDhoBo6EnUzHX4UUfeCaB2fySifoDV+FJ+ycYjwd\nsBY9RC/8UyDwJZlCBHkQI/p7hBdLP8FNfuCDJ34Oiz9ga3QKTczDH3qBThUx6BC/xp6CIdYgf/lP\ne0ADeqQgjN7GwOsvf/nLsb79KrA0Bq2SMzjgq/3YqzafHR3rH/QB27iace47g2OcZqz+YE34zTFu\nXrt/1QaOozbXhpd7ck336QX/KgenC36hhv7sEFmEuxbM9tC5NR7PL6fAzpNm0FUvFhXJ6Cid7QWf\n4ho9oD/RHA81MhpM/KR39Ep+wvboq/HT8jEvHegveHtLNvSLUei9DyjYWnUafcakG66tH1762X2/\nPOCX6TI7EI7gGuve3Clbnumj3+wT2VSLcVytOFZ9iD12pFf2wHZbh70QS5yWTbJmtGk98DVr8HN/\n/OMfDzqJBcAh7x9//PHhQxQlzZv7dO/Qso/iOYdYgx3GKy//8IvuaPZqvHWd2UCHa/CtbayDjxFL\naa3FvinweqGFLhpb7xceiqjWbaxn0Zb/ED/ylewsv4ROCszyMbGP9Yx3aPCB59zr8eCe//PWFF4T\nBEyMqZwmpy+QVrTwhoehYGSMqzXePUEAa571J8RTaPQbW1/CQ7kJNQWVlDo4cgGmg3GiCAWAjI3r\niRPY97nFD3uY9HVfUGC/jICAheERZKKNIJKhQqNJ2x0OWFpreR4PnN1bo+fWEqALgi5WYMaA4EvP\nwYoHwdH32N4MBeJn9O+eo5OwKLxKhiXNCq++GBDESiIEvt62ctASMrpOfiQEgjlGXhMs03vBMMdQ\ni8/dO8MjHOJ/yb4AQoDYVxTGN7a5zq/aOEUtuxMcAYCvOny1JpGhO4osP/jBDw6aSBqjX3Me0hmN\nHe2xsz3ql0BJpNCIw2dfBfECMYkOu1KwZQ6+myfw1I/uDgGeuRI/CacAgs+oWG8u3mjmSRjJAtsl\ngJJQOp+eJ4bG7bzUtze4aO1r3ruuv3nwhyvcfJmoIGGdAjAJNT0w1+GZOc72KZBS8LBHASddIkPk\n3Bh9EvoKE9YqoTafTaZHezCKJvwqeWW7jdXA7BDgWU+Rlt3V2iP9Bdf6M9g2jt0219rwMhZdHPiA\n3750V9AEv2bfxuMh+ZBglBwolihUf+1rXztoJ3C2rrH+htez9bafjWkfwXS2rgAS7yUU6Ad3++4r\nKOPsOz7gCd74cyW+WBco64u/1pHcS0i9TMKfaGMfxsVPa0v42bqL5cec4WHvxtAJ8gGOxIpco58x\nxitUSYrAIQ/9XUlr2jM67/hbv6M9OWvhdtw8/+dc33Vjb9KPlopXeM2+g8/u97M/NNKMS97JhuKS\nAqm59q94owBb8Ua8wdf4sxN9uSNhsDcFEvIHjlgOzfghcnZaek4uyasETSIkcVMIE8vRD7ZEEUwh\niQ5NGYAXPaOLeGUcvB38HdtFTuyXnYme8HLdvXONDJFhMo+PFXV9bcN3kC97V3TnS8mBpLXEKzgT\nZn03OcM9/PChll3li/lzL0rtW5yHxuwmnByK0hVP0ZFOsXF8vaK4WBlPeuGAPnirWGYf1ofHtPnh\nkS7DLfoZP3FtrP4O8sG3+Nt6kmZz2Q5/Gxkd2RJrojsc2RpySbfIGX/txa1EHb3Zv/CzhrbTHDxt\n4kZWwVQYIGvo6Z4MojG9R1MyqJFbtGSn0IiPUlwhA+wd+bWutciLPEkRBe72XIMDWGCwYfaD/uDK\nc9gLOmP/ZB5uYi4+lL21R/s1XvyGDhJyeOBl88krPWJDyap+uoBm5llbLkWPNPvGkz/84Q/HHHoi\nyfciTZxvb3ST/OOJgh8a2o8DPvjnhbr9kCHyCF80MZb8KUQoAqE3+nquGMwGk1eH63hlvb///e9f\neLZ8iH0rWsg56JucI30DJ/rbj2uH5pk2713Xfzxc/7Ar4MGXDPBD1sQXtLWf8GqOsXOt+l/nDK/2\nE87Baz3yyc7hheIRmWHn8MyZfU0mzW2vwesc3MfzyykQDY2c9GMLxc0+WKDv9MSLa3btYsUI9HPG\nJ3iokSUw3bt2prd85yeffHLYanprrphUHO7lFL0li5r5jubDywEnNoSukRE+i87p85ysdwRDP9vk\nxbu1xAZsG7zogXHGND5dcK+JJdk+/ohssnvWtCf+mW6xC+woO9+vBdgi9GIHxFFaa3Xdmu75Mi+I\nvJBhT+DBN3z44YeHvRIPggNXbdLbPZqyrf4Mg7gOnuhJZ7Kn6GB+sbQYxFE8gr7GiIOsx16Ka4qZ\no4lfSZAJNkxsCKZ4xYtGX9eyx1o4eu4afgquPh64XB83yAfEXvLm+VXuMfn5/PhR30M5vzWF14Q+\nwSWogjTOnzA4c+qEMoVorHsCQMn0ue6ecOsT0DrAnQKXoBhjvoPDo6gCC8pK2DVBm0CJIeHU4QIm\nh+kc3GDe53O0tYcUuv30DG0rWDBgxjGCDBNDiNaTVzucCQ/Mxta/nzl1AdY31xvxd9bPvSqIN9c5\nHrh2PLY3R4H4GZ27FzxyggqvkgFfJXAugmRfF3BYAnNJgoKkpIwugSOY5hA5FI6mRKVAvjWc93Xb\naf3OkiRBv/9EQ8JdADHhmGds84LzsvPE4dxYsiggUjxRDBLQs0uc2U9+8pMjAVJYfMgNjXY6TToL\n3iV3nL1gns2g276CFEQKmoKRvRWIZH+dJXDg8BeCLoGfQ/DlufnoLkBhpyRPp9PpCF4kXIIxcqG/\nN994t8uEe7D2NvfT873PPZj2wH9I8Mm9AIy/UbTwxp89lXTVzKMH8DffnhQ/yJJEyFjJLZ0yVqBF\nTvF4AABAAElEQVQmYMofCV7ZaWfzHWglARVo8V36NHQE01yw2Fvw0cRhHfcOz8zDC/PAYvvNt8do\nbhz/SQ/1eW6sudbgb+mDwJuOzgQYHl68opMEXaKBn2yDZNiXAmwKHQLHvtBG8dNXF+yQNa2jtU/3\neG1dQaz9sC8V3/YXu2gvFpBc+hMY5FOxFk01a0goKvpKOhQv8M2z2dDjtGQPv+2BbRJA41+N75RQ\ngeOrFjCsZ337VShwD2fPxUYKJeQfHtrcq/3ag0MzBswdt+Ph9k+0q3vO8Ww+D+beDxdJiaInGbdn\nMkLnFVPsI7jO8EQnuuCLSMmY4h4dZQ/AMo6sK/Io4JIRvJ+yRebEb3gt2UA3sMCVWJJ9MR0Zo1cK\nXhU/yICiDdtEX/GS/YCPrz/YDHhLbHzFiffR3DrGKNKXAKNfe+y6vUZbNCHDcCmGIvMOftDeyQC5\n4UclbfYYvYM/eRLs687mGD/PwXGGk7UV9cmYZNqZbtEJhXBJrJcHzvw3/qCFeeTzciVxvsJET3Tn\n341XAMRX8QF+4ztczE1WX4Q3/IyL7tFhzjGGLvItElG0pbcKqV72XKwiBfuB73wGefRLHPyEF1ml\n7/SVDLGBbI12bt2J+6QjPNk+NgZNyKqXyWSObLHRaJBNwVe28Mn6QpuNckZbuPIX0Ye/VGAgI/Ik\nBRTw2U5j4CCXsQ+FQ7JJbthQfDKOL+A36QEbnvzxTXiowc365JndlPijD1zICH0BB4/ZoXwQfrNv\n5BXP7YO9hRf9ZeN+85vfHLSnU77I8sKCnuIJWRMn2hfY6AsXe6AH7CG7kg84kF3/wMl4cSjaKHaj\nkyYGgD/Z7WVrxQu8qAjlJ8HJiv/B3DrmRld7mLp2nR5Z09h4233zzSMbYkU2RatQRA7KYY8H65/k\nLhmo/3XOcOkAZ+pSe4QbX4MuYlq08uJE4RUv6AeZ0nZY+oLj+rHdjALoWJv0Q3t2TYHNC3x2hW6x\nqfIctjiZNn+XmQmXfxNzyMW8wKQndFHhlZ2ku/SL/E6+pgfgh1v+gt7xGfSOT/OcraLzxYpshMbO\neXkiNmBbqrccD9c/rQmGw15aT/zJ3vDT1kQT92yHxkbZi4P9EkOwfe7pMvsFljWCf0wc/3jGTsrh\nZjGTXvIh3/72t48XYmBpUy/N1eBs38/WyxzxKdtvPXP4UbiJsfWV09gbeqKbaw3uaiDyaWe2lY2Y\n/KWnfIEir19/afarbuKlFttp/MQTX/gAsRT/x8/bH5snjnGgHdtbiy8TTs/u+/mtKbxiFKHTCBoh\nxXwGnrEXFBBCzE7hjSccnDvhExQRKIkFw5QwgkmYBYvOJR76warYSvDBERhQWIc+BifBFOgzTCU4\nYAQvJdN331u8sI99X56hEcX3NkwwhkYUXpCjKMDooks8MGeHE408c8yxPZtz8MmbG2+fOQTGCh54\naL42YdQXrMfz3VIg3ux05lDprsIrhy6IFPQLkgXfgrOr9WUdp8DZ+2II38Chz5yRA3wOL50N+8lj\nY8ieeQ4wHMY4FCgkG4o6CnkcVwFE48BtXmu87DxxaCwYNXg5JDIcGad9uRJQTSL1s5/97KAHfB5y\niw6TNvNaUKY476dODnwXgPlCWZGDfmvsr4CK/SU7kkNy5mCPwRFAVoR1Zn8ECgovgg2JlqRfoEcm\nKwiSR4exbAz84K1NXN1Pvs9nc3x7tj6fRYY7Cwj5Nj6NjtgHGRXkCpgFnvwYGdUmXPtTEDBXIcOe\n0cd4PtBY+3bAEy0V9IyxLwc6KjYJiPlUutU65ltXQCoYjE75wWytcebBh3xLGAVt3rTzsfaNNvRR\nwC7oK6jEL2MVHOBonASBL8FzNuK0CpN4YX+CaUUT//M1OvpqS3KOVgpo+Gi9ikOSZnaFnIAf/Y5N\nrn/AlWwrRJAFeOGJQpfgXfHFHjR+BQzFO/aLXCrCkCe0rBlDBhXgfMEv6MXjubZ94ocv1uwR/uwA\n3pE9ezAH3uD4ulcBBW5wVojga+GNN3DsS0J4873iJuskw85gZ+88R0PnaGO8w739avAOd8+69sz9\ni1rwrN1BfvDqYhW5FLDgT1YkIJJ4MlSzFnxPSwaik6/fFB3RD0z4swWKnooyilhkOZ23D2PRl94r\nCNB9uuXeGuaTw6tlS+CCLnRI3AgOHeG7PLOedcFT+ORH0JXM4LV4B5/AFZP4D88U6K1HttvT3KO+\naBUv6BOcyLw9sYuKR/ZjD2RQYk1u6WgvW4IDPrjay/h0DFr/hAcctObZM5lGV/7ZiyIxHZ0058lK\n8vGTPIrJ6AR7Slbxzzi6RB/NJ594iCZkGT+K39iJZO9A4vk/4eI2Gu3PzWvPnaNH93RKgU88IuG1\nnmICfWJ38B2dfcmr0EcPJZloTefRHu7G2duEP3GEW2u69mzeFx9n+7M17CobSb4ckmnrs23sk+fk\n2WG/bLhCnUInOeYT6ELyKkmHJ/qSV8kzm0fv+Br7QAM8St74Itf0gPzjvYPfQnvw2Cow+QX8ptdw\nskf+gE0yB/3gR57JEdpZnz6TYXulT+bQ31//+teHLYMXXTfGGvhGB8gdmJq57YktRS80Infobe/G\nVnRVPKbD9oSO4FpDzmIv7cFctIW3P23lb5jCDZ3o89OnT484sqKQPTt2/sNx57u+68ay+3All4pG\neAInesLX0fNsODja1NVz63866nb/tp/wDG5n/fjhBZeYna7Aiw7REXadTUCvWjCD0bnnj+eXUwAN\na5N+ZIDc8D/9GTNxCt0QH4mdyVHzzZ3zu/acDJI7usI2idHEbew6GaQzeD3nmEdf9M01XLMhdJ8u\nOdgQY9kC+in2EzPysWyAtegkG8Hu0VMNLPB3eXev3wEeG6juwA6CS9fJof3DnT3tAzqwPWO3nNm1\nuVZ70Qd+TVzAB/oq2Et9e7Qn/u+73/3uERe+KJeDM1qID8U9Yjv0sAZbCJdsGH444NLeXTvYSEXX\nXnZ4gSQOmHjbv1+GK8j/7W9/O3wv/ilu+4UYXWXL2x9/crXiID5E4ZXN5Vvos1hHrUUObY4918y3\nbnDqfwjnt6bwGvMwklALUgiCNzCElHIRVArD0RJWwiSwJRCEjxIJgAk4xSdQFBtMc92DTYk0QkTg\nwSsJL7jS30H4wZOsSvDAmIL+EARt30P80L/vlfFiJL3lFJwKENFcIClwdQjY0W2fu6+z31t3rg2G\ne3DwS9Lh7TOHLwiL78HJUO1wev54vjsKxNvJL9A5ccUDiY5gsiRGsYGDpbeMu4KKoMHb2nhsPrjz\nXl/8bM10UyJBf8lBwbwEgB2g+16kSPS8mSSrnDw5mmskY9OpWPNFzfxgnMM1PNFCEcXPSzhCDhU9\nfv7zn5/9ezsvWvM+Ppt0Cn/00jyTqCk0kAMFavzxtZjAQnJeYERGBIUOCabAAJ/xmF1n3zX2mzwI\nutglgddpFXHYDecKBMaRl+Afk9c/8c05meuZc889Izf4yR9Yn19x6BNswqsAVIBonINsSnIVbiSJ\nZNgLCUU9CZdkC27WcpBL++R72Fj+UJAMDrkn44I2OAiaBV7w4ys9d9bghm6Kvewzm20P6QOdIpuK\nZOilSCVgLVkHE2x74p/BkdRKyBzws2+NjtuH4A1M9LZ3QbegUNKATpr14YgGkjh6ik9gCaYleeyF\nfRojcJQQgEl+0JHPUawiSxP2scD6x9768kqxRcKONvSTPKEp2grc4anBy575OUGrwoFkBG/yM3BE\nU8VPsYrEFC30m+vAQ+srFPBbvh7wRQo/ip5kQHwjvlBkUFSXiJNvc42TUEmmyDOZRXsFLUU/NDAO\nXmQfvRU7yASeWwOv0d5+yYnxDns0Dy3FUSUm8LaOZ87ua11HA/3G4SG+O4vLHHhGhtCbPOGreej1\nbH394cteONXARit6wF8I/MUaYNkL2cNfRWdFPfufRXzz7cleyAgbYt3T0n2FJ/tDazwnh/iNNnBG\nM3tFd2PoKL0FkyxLssgemGREsQq/yZ89oQEZebqKNGRGIobGGhiTXu47jPHM3sh7+m19cqRARGbx\n317EqtYKdvCPhdY/+sF+WTPOgV5dkw14kC82Jr26WokZ3p4WHekAGqRHFYes2dozHkRnzxQw6QDb\nws6juX1fh2v9xqBDNhS+5BUvXdesrZnn6J6NUkzztRIbYQ6ZhAv7RE/YLrEKm4TGipV4TQ7ZiblO\nazhHN9eNiQY9bx/0lB8je2wNGTMWP0+Lrvko9KTH9kge4YQn9kFezXO41uc6m8fvkReyL9aSMIPN\nhtsnWsLDyw48JbuKFuwOeNZyoHd2Ih6ROXiJu9ENDeml5/gND7aQPtMhdgcs8+iWnxL7ZQPbZ290\niG1XeGXz6Df4bJx9hCca0SX0sR/xHFni38HxnH+0J/SgQ+hsP+jGnoAHb7zl38CDO9ziE/6Z78Wd\nF31iWDLvizZfiinWkoXGO8dbOMz7aLY/N8ZYe+dD4ckWwpnvzm7hXV8V4xs5qLWW++D37FXPYAYX\nTIf74OMje+AFhuIRvNHUr074bH6Z30kHmj9hBOtVcXwb58UTe9/pJ25gz/wZFb8eIav8jp+GixfI\nu/kOfIknyaa+4NM9OiwuZGfpBjtU7DfHwsU88IJZX/y3hoPcNNYa7sGnl/y2a36APpIn8cLUyQnf\nGt07a/SInWDLwGNzNHaBzWEjnOktPWreMWj8Ex2cw7ex3aONoqlfX9FXz+H7wQcfHHaNfWBrghV4\n49ACrmyKPJDdK75AM/ia69pYLVq61ocueCIOUAwVj4inzAtXY9GZbIiRFIov1wc/chF+W0zlK3X2\nEz3YeXaI7xN38AN4g25eTpInfh5/4os1tLnmpz0P59+3pvAaIwkt5cR8gk4QCIW3I5SUgBYIEUzC\nw+BwTJ5z/AUADInkFDyCC3bn5nL0FJ7DKxCkIASVMyfE5rgnzAQ1xer8cMTt/3YylWruk/IJ7Dha\nwSkFRiNJI8V1CALRDt3ONbAnzDnGs7k2GN3jsSTs/fffPxIcRgfPdgMF3g5nrvF4fTcUiIfxB1R9\nHKE3sf/85z+PwF4gLYGWlAqc6awCmq9AFdwEzGDgNV46wHG/r2EcRyNx4wwk0/RXHz3nROi/RIoT\npt8ciL/nxolwXMEHq3VcTzm6LYWSdXAcNXaMk1V4tVd6Ae+f/vSnR9BKl+b45j2UM/qe46H96Scr\nimV+2XC5AgTBu8KrAEGhTGPDvYlFP2P4gmQDz9gFMmWu5EyRB10d5APPyUvBl/ETJ9eTB+7r09+9\ns2ZtsoaX+FuiyW/or0gAb3LIJ/FNbCf/xWaRTXbSM0GuBF+wLCklH/bTutY0jo0V8CnsoYHE2J7A\nRAfrwwdsc6ON+XDKp0pI+TO4ask9XVFQgUN0FIAJLukXGPlXiZfAUcBrH4LebD7cBbv492Ql6OiP\nZnitoGMPdD4ewgGdFVLoqqCSnVeQqBBkb2CeVhGB3cdDuCg++jJJAQ59jItP7Q297eudVdgmVwoG\n9s5OKDrQT2c47f6dbfGfEvgbVwJQOGpwNxYMPk/RFRz0qGDR+vBBC19BOiTVEhow7EFCq7Cs2Ora\nGS3jIb7gCXkW6yhqoKPiDZpq+smNdRQXyD2+kkH8JjP4RVb5a7DRHB3FPviErvjtPjl1duC/sRq4\n5hYbufbcOHg0x5lsz5fa6A4fhZZnq/CKb2RRA8eBX4ojEgvJjL2AW3FHPKhYgVb4nRw1H35klgwr\nXvA/cLBnciquRAt4oKX5xtMlOgqfKc/2DY54B+/QCF0VXn0FA5a18ckLPr+uUNQDU4OPtXc8PQtn\nz/GcvZhFI7Rg1/DHudjXXM18cx2u6zsuXvJP8+CnsVFoojDoowdFV/E3uJI28p9tICtkmD7g/Y7H\njMXJqDH0Di/Nc2/9vQWnvZAxdMF7+sm2kSs8YFfIxd6Cod81fnup50WwZFRfcm8fdBxMexHPKuzZ\nK7lBb3wL1nGx/dM+wHXU6ndGY7JK/8gWmpA98mZd/slB9+KH8YpxZELxGk+cFQfFOZ7RJ/PRlYzQ\nfbaT/rgmk/ZqnPXAojfBYX/g4iD3DvTGP/ZNs384wZUNBp8esDXogzdsEV1mZ8zPxpgfXfzv6O++\n++5BX/RmJxWOFF69RLFmtEIHvLEG3VNIJoMVXOmFBkc4s4fklu2EA9rYOxin5TPAoT9sJLmxFy1+\n2SMa8U1epog1+Bay+r3vfe948SaugLcGz+Z2X5/+XWYaiy7oxWbYO5/Bl8OHfc//2mcFo3C1TnBa\nc97re9UGd4e2w3SPN3THf9rjgwlyg6b+LITCq7gevho45CWYwet8DHr850YUiCcGT/rpxxN6LHb2\nooBPwgeFV7kWGdKmLE6egBdM/dlaZ/ZZHEf2PGvcAXD9s8NxXzPWmhO+Z/W5tgbdzcZYy8FWmRe8\nfV1zZwOHnRTbsjvmgcEvsBFs49QfcyfM1gmme8fE3T155nvYBn/n1Qtj9sI4Pl+MKD4Q70z4wXUG\nh+2W7/Ct7BU7Dkc2iS1t/8a7tq5DwxPj2HVxUfGAZ/uaaCEeJhfPVpxlHXR4snwE2y0+EROhnWfs\nJ9/vnp/m/xR42Xpr5q+tYx/n1jw6H8g/b03hdQq76xRTsZNjEhgwNPoJPGcrkCBgHLg+zpYQSYbM\nEeAwRuYQGALsTAAdFJPwca6E3jNwC47AB1cDI8NhnJZBO24e2D/t0bZSNNeMBGWkmBIb93gj8OKY\nKXs0M/5cA3vCNGau15x9DMMsGfvoo4+OIgVHzyAEzzmeuD4HM9iP59enQPyZdNZH7y5XgYzBJxcC\nNG/ZFFQkbhyKJIJT8FNTuko38S5n75pTnvw0xnN8F1ScVkDNKQiG6SfnK+gWPLMB5JB+c4gVXiU3\n1ocnvJ2t4ToH9yqUASOYEw57IoC3V18KsFfw//GPf3wUGNFm0u9V1v48z0ETR81e537ZWkUvb4EV\nMvCHnFxcXBxBAtsuuFSk9xMfRQkNDDRnxyVpErNeBrmWiEq8yIux5/i84wXu7HNvDQdZggs5I5d8\nEdwlsgIW14KW5MBY/kNRUlBjDj9zWjIrwdL0k1P4SSq9GBA4SxZnggcWmaZL5JtPBFtjfys+GCc4\nhAd8O8gc3ZAsmQ93eE4+0AnFDHhIMAV1DvqlgITO4NorXHwp5Wx/1gSvBh9zBYi+urAX+Nov/yD4\nREctesPFPIkx3yIBpePgCATBwU94mCN5VaxkYwTD9hasCZd/UITwZY4DXLhI0OmlLxDInTjBHqbu\ngkOmJN7sF/poxjnsxwsksuurdrEHHKJr12CSaYUHL6DsR0MD/JeAK0QKyOkC+1mzX4UheoE+7IkC\nVEUXaxjjOXorUrAp7jVyKckHV6Bvfg2e9KOEH73NxQO0dtCtipLGkhPzHNEBDvaI1nDpuXVce64Z\nX6zgb9NJ4BXF6Ea0Ap9Mn57rCVupD97kjfzhfbpoXXNbo7XRSuHMfsDQyIii7XyB3rrhbRy+wLU9\n2hfaSFjID50gQ2CJeZzBsZ4Cva/1xUjoB0YNTC0c571+93QMf+movaEFOOJT941zDk66pK8xc139\nL2pwtza9JM+KlF50kWfr9vNVfw7lyUrcyEPrRD/rdW0tODnwiR0yHh0d+GnsHL/jBx6c0AE96An8\n2DL07+XauUQXXHOt44w/fIeE2XW44znZAIuN8PKL/aVHdM789gKf5tXnrO8crdufMVpz6w/H5gej\nfnQTF7FTFfjJWrbbPPiLbyTRXliRT76PzUz27B9MOsN34u/lis34ATqFH+wzeMbyE/rZdLTGO7jR\nJzQR8/upK1k4LR31jF6SFwUg/gCcmufoSI7ee++946eucLQe2Ar7v/3tb4+/VckGa+bgsV8+sLvi\nN3LHFnmmWQN+dJkNYdvYz6v14os9wVe+DD+d0SQ/lr6AhTbuzbE+GvELXqQpjljXf6CjkAUGejUv\nXOADjkOzt1r90dca9kzHfIUttiFv8FTw7yts+KcrrQNW18G/q3N4grev4Z48KKz7MwO+sCQn8Pbn\ngRRe6Q+ZiwbmBDN4ne8K57cBTvTc+UL+HeJPH7nIo9jI09JJOiPOwJ94YL5rPLiOD56T0+Y01jpd\nT5rTG+PPwZvPWnfO3a+N0cBybc3s7z723H308AxOjlqwX4ZH+4B7+63PXIe418sHv57gH9lgOuCL\n+H7CD+/WctaCw86I79hfcZl4iA8Tr7Fv5SvmgOOe7XUNhpqV8eICa7dPz9BAc822ik99Ce0/L4Sr\n58az9+KYXmhn/zwT+/Ej/aqATscH89tH6xwLPsB/3prCawzdBVUAwshzWAIC1wRXMMbpSiw4BQLt\nWcmwZ4INgUrBA/kgRASWQHPEBFhQQLiNA4MyWM/aCVt4me+6A0wCO+/13fcWP+yjvbtGM8GXYEaB\nwDgJjQBRUItu+uYc82a77vnL+vFKwCcRFvwVSGV8rDGND3iP7c1RIB5POqM/eRCI++PeikUSG393\nyM9sJTd0SJDsZxsCOQ4i3tNLASfYdJpTcI3Hngn+JbgS4QpUxkiE2QWBt+JOxQsBrDd3vkKSQCqe\npMOtCb7ruY+bUK15xtp391MeJdICVnv11St7RG5/9KMfHf+BlH3cdt2b4PZ5GYMmjtpOZzZa0iQR\n8eUg2kgi6Tm5Ydt9VSawVJwyvgaWwIBMmSNBU/AQPAhi2Pl40px5Drf45T77YRz7j38OfCNjznBi\n5/gafsYhCYQP2QLPtXESK/LouYKbN9WCHjKerJJzBTOJpqBHgic4Ch9w+kmoYM26cLM/gVgBGNzh\n6pkDXPf8JnwF5pJ5uGjhaR26JYmnL5JeeoKOzgWEYNFteLP3cIEb/YuG4PKldJOegoEW/KqimYBT\n0bBmHlqYY50ONOBnJHTo1c+4wIIv/3+5CghsjJc45CJbAbYxGjzeWV+6+lMBZErQipYVXekmvIwP\ntnmu0cSXDA7FYDypSXh8damAKIEGE/3tp7WDaX9f//rXj8OLw3jrefJPtn39pLhib5458JaM0ws0\nEpuYg7dwBBs8dFZAPa3Ey54rHJFNhRE6hl/xHmwNTMVJCX+FVzy3psPLAjzhe9HDmpr5yU99+tHA\nffi77p4MKpIomCuW+8oNfmTVeOPoj3Xwv+IDGSN3xorRgp2uRnP9GjhgmE/n0M4aeKZ4N+NB44Nj\nnhac42b9A5bCv5iHnsLNHLDEPFfPiz3or0CjyE5e6ZNx8NNcz30enc//6Zkz3jqn1+bjsxZu4aov\nuTeutfTfpFkHbcgUPWKHyTQ9ZVvYVX6bbaKH5EEzT4OHA176rD9xm/jCc44P19nveffoQN4V8+DD\ndqEJOveCgWxq1g4Pa4YH2fFiQwLKhxRrgIM/ZF8hUKJJN+mAPeJ5+B0LrH/AdVjHel231nzeHGNn\nM0ar317nfPdwhqeXJYquDraGHTfPAT86/mQVBiX8fB+Zk0zLZ4JvPbzlh8BhZxT+JOLWYg8d5Ite\nWMPZM2cNrfgE9pyvFXs7W4s/BM/LJy9S8Go2eICtsOj/ZhAH0iWN3PEjv//97w+549eMRw/wFTT8\nQgF/8Lk9mWtde2DT6CAfa202xv7hKhbAUzw2n23R4ps95hvZRT6Kj7QfttiXwadlT32lq5jFloAx\n8QjWhHss8vwfzx32JH5g/7yk80Wtwi5bi3/ow9eRa32ThxPem7qeeJ5bg88mO4pOXmKgG5+BR+wd\nHsFZQ5/gdT/Prh/bzSiAjrUpd/rYIPaRbRCDkH9xAP10kF0yPu3L5Ev9YOkHf7drnmkTzqc9531R\nzzqDC6aW/nVv/dnqh8e+1znuptftdV/HfPtpnblWdPBci0bdi0PELfI5foWv8OLBV8Zi93x+cMzv\nGjw2lV9jk8Uz7JUx+CZOYWuNDz+2k62Pds6OCRNczZzwFauLS+Scf/3rX4+1oq812BiFeTES+y+G\nZCPZSnZI7M3mG4s+zZ20cn0Oj0+xud//vlWFV0zUYm6ChOmElWPmuCQSHK5rjlK/55wnh8wpuCbk\nBQ+JAaElTAm0tdzrN5YimJugmRdeFEAwao6gwZpw7HlrPIRzPJj7d40GjIs3NJQVzRgjDgA90MJh\nfufoEZ16Vv88e9bzxnvOoEgEJTYff/zxYeQkjcZqrRXfgnM8fPznjVAg/sQDi5AHhQzJm7+FIygW\nlPkKSNAtOaBrnIJAnQMT4MY3PPWcXjn0W4e+ck4SI8Gpa310n/wpVgjEJWeCRHM9lyQLvhVeFF7N\nA3/Ki+sc1m0IFQxzXGuTFuEuYFUgqvBqjz/84Q+PYjDawOehNnSJTjtt3AtA+gJEMZ79ZV8kZ854\nKbCUoElcwMJXzyWbiupkSjLOJgn+0XPys/XR2HUtfPThFdnlO6zJnjkER+4dZA1+EmO2TrMOfgqa\nBC6CGDYSLMV/e+OrrCFJJouSLPMU6+zZGp5dXFwcX1jam0KXRo7BUazqp6YSRHu0Djiu0YQfs44j\n/wRPPtEc+Dvbo7kSdzbVPOOtQ5cEbeDCoT3xe5q5CljoYE7rHQ/XP+aBZzz+oUd0tT5c0Dq86aMA\nEP/sgT9nM/Aafnh7sejiz0+ku+DRdXrlCy5j7Qet4Ha1bIsGJhmR2LIZnivuK7YqvLI7eKlfg5P9\noKfipYLTN77xjSOBRwfyYs/kQvKs2AAOnPV7Punh3p4U7PwHDBJU9kizhmZttouc9DdivayyRw0M\n9CRjzmjX4V7DJwmAINqB7ugBJ7KliKNIiP7t1TywjSNv5C8dIst8O9mwb9fWNx5eDjgkg/boGmxH\n8Zf1w9UcuIjZFIH6enXiAyc8AM+a5hZjBQd9a/DRr8W7nns28bM+uQ6O57M1rz7P9TUOrdGWvVak\nQyu4kUO0dSbz7NE7q9AveSG/WrCC3bk1PW9Mfa3d+s5oZZ+1xjZGf31zXOP3M3qQPfJGjySU7Kxm\nn3THV/h0kG0lZ8Hd10RXzzrP5+CFl2vN8/b4ac+nY+pjX8gtG+mAJ1lV7GMvHOS2eNy6c01wwPDC\nQVGCrvIj+umHPdljh7hCYc0eyQ1YjvaD9vWFb/uY9+Hv3PP63J/rtwZc2V76wRawUXghlmKjjIGb\ngqLkOBqcVnEF7myj5+QUH+DLZ/mC074VXdk+9/Ibz+2TrvGZzvSVTMOR7FrHy090to5Dn6TdePyg\n64qU4huF7XwHGHgDT/ZXAZXeoLF5nlsPb//0pz994d///vdhn8Bli/w6QMxob/EE/eCdzPKd+Cvm\nA4+9gqs51mV36SyYaBL/wIA3+VLQR3f0RTu0xgM6od9+/Yc0dAFN0Gw262pg1/Y+64HLf/sbqXiL\nB3ySl4HoYs/gz71OmHMN8D3b12n9VzmDFdw5v3XEafjsa1c/s7YfuH/5y18+itLwRz8wshE7zH0/\nc53H6/MUQMPaOfqRI76HHpBX/prO4gd90OKDa7xxn12bMM+tpS8Z6Kxv8rh+8F0H21lr7HGz/TPh\nexSs+huuv+aZVp97x77OhNEc8+qfawUvmI2r31lDbzZZjKrQLS6ix6dlGxUq2TytvQevNT1z7Tm7\n0Di453eMmQ2McO3a87mn+RxcsRZ7359FYO/CAY5wZtOcxTL8B/vTi2pjoifYc73W9Rz+E6dj4AP4\n50EUXmNUDHwRX6YgmidgFkBIbubbTdcUQLJIqMxrrrO5rWs9ayfYE4/GNJ7Qat0bS8AkkJIxZ8EJ\n529t17MF2znYntdfX+c59z5cZyDQMsXDI3S76Z52WrTvYEe7yQtjGAVvlvyNV2+zBVTWdJjTvOB1\nbr3uH8+vT4F4PWmrT+B9tQoffnbma1aFEH9eQBAt+BYQmyPgFYQa481/vE5H0+V4KxF/soIJh+Ba\ngFrwzDZwNO6t79A4RTrr6wl/i8rPb9xbI7jO1oIT+XvVBo426ZEdktRygAqvHLcA1csDAb0EED57\nC94Ocx/3eb2f+E8c0cczhyIcO44+CvUSRDZVw1/6jV6SUvIhYZYoK1x6u0yWJOL6yQe6gj950NrW\nww/JnoPNcpZAkBuHa74GDmRKIDuLheCSEXjxAwpUDomrM3lrDbDMtz/wjGGzBDveJpNRb8x9zSsB\nlOApzCncCITsR7NvhT5fP0k06U30SG7tzd4ltHBLvvXXjC2pNhY+JXpoAUdfDQngnY211/2AD7qB\nPeEb1308gEf9nllHs761TytYxU97x0NFYXv083/XmnHsB/3FdwVG8PFLwm2cPvP1SxDZHGvoV6hQ\nKEBv8uVLS3QXOBsLp+hovmTFlzyKZ/NrK7jgKdiS8sv1tS27peiGHuiiAGyd6EAeJP9e+igcC3Lj\nq7UaBy6Y/latAgQaoD+8NDQ0Hjxy56xZk35Y037BFjCTAXPgBSeHa3ttTfPBTJYV1tCwIoz5YNFD\ncmWs9eAFTryMx+Dpsw7ewMt4e3B26Ce7eAaOsRrYjnBrv+3fmMkj4xzmaF03f/aD5TA/eHO95hyA\n1j/Gaq3Xc3PQnQ77O72+BLUn/KczClrGKKJcrBcFZAhNJy7hOfusFW3YHgeasieKRngaDhOvYOhz\nPcfo0xrTs+7bWy+9FPz9XJU+kX/65ospNlYST+aSqfbw6QrXJ3/7mvN+XgfHmTyQDbE1/VSgVwQj\nNwoK7KKi2pMVA3iBQV7Rqn2Ba2/8ityATtkXe6KISSbx0AvgvuAFB5/AJ+fJHljRdeJbX+fGhUP7\n0d+8ZMr+yH0660wf+Bh2hSyx7/C2d/wBlx2zZzLlcC0Wzn6TkXTU+vQQDRX6/PLIV/RXKyazDluh\nwQ1e5plP79FOnwICu8wXsbnlPcaxB2hoPvzwSWHbF8W9QDem+Yqu5EliT7bYqRpcFPP+/Oc/H0V/\n9GEjxYp+ocS/w0sjA/ZEHtDIuvZEXsgAHOmmFx78iT2Q2ykf6MIW8nV8Lj/njCf5bzgoZFnD/uAD\nF7oAf/yI1+il1RfPu/fMmmJTMuhLUT4DnC9+8YvHSw38pOf2iTbJClitA85dNXC1CTu8nVt/jtFP\ndvpJu1gN73wF7D9Msxd6tM9trQnL9WO7OwrwHXjBb7B7ZKjYlM2uxYv47r7rxjg3bvbt466b25z8\nS/c3Pc91WqPzhHHTvjlnXu/z3dcmDvXNs72xF2jtIPPs4oy7jJ8w5/zr4O84zTnz+iZw4cVOyqu8\nDGNj+Rp49ksAv+BiK+kt+5P94w+mzTyHVzjYy7nnE9/7ev1gCq8x6Jzg7Yx0z6AwJgIoyavg1sEh\ncsCcGYNDETpaYzK79ZwpSffGcIpa63c2xnXJDCcuwRfsEEqJsLcdhBsOwTQn59P5WGD8A1fjWms8\nemsuo1cbjhbRmxFwLQhyCE7xSnDl7/T5X0b97I2RqAUzWPU792z2PV6/OgUmjXfaCmwFxX5K5c04\nJyB4V1SSzOEhnaHLftboSxRJxoQJs3lvDU6DkzBfkqSxD4Joa9K3igaC8WRGkKu46c28Yor5ZAv8\ncGdrXF+ns8diL/mHXoMRTMOtod9XcQqv/s4rB2h9hVd/d1aywKbMNveuf8Kc4z7P13MP8J/3dBl/\n2HW08aWVYphCpWeNNU+Sx/YqVuKjRAXNFN/xVnAZfZy7DsYsakgeHOTG+hVaJXUOsupMfhzklExI\nJCW5/IAinkBFMkwOyRz+GQu2PfBPkjaHtfBbMih5k4SCIdFWdJUY8yUKCn6yJNklo+RdIz9kRlLO\n34ANT8GfNT13wBMt0ItddG9dB/ySMfee2xM7aw5Y8CwZtZa9lOzCj55ZR4u20frofP7PfOb5vLe2\nYgk9Rg9HfGTnJcKKBQoF9qyZg16SO4GiBF5iIdBFI3wJRnyAt2t7s08yIG64XMVSRU2xgz6Hfdov\nmvLv5MuXPAoGiqb2jDbojrcK8r3wRTe0hCN5UXxj+yTu9m6+F4UKdQoR8NbvANee4UoWFNYltr6k\nRQfPZkNHa4GBx3BHI+McngfbvNaIZxOW650v9uCwhrnBQ0O0dB/N6Kij1vh01zjrmtM6+7m5zuHd\n+ODNZ66TaePAt47m2hytdY6b9U/9+7PZ3/WL4JhP5y9WUdWfjaDLbAi+sWG+UoQf/VWQIEcKenPd\ncLOewz0Y9M4XwFerkOTePLJDZlyDW5sw9IV7/bPvujnGmkem6YOXEXww3WSHvKgUX7G79Kc1XgS7\ntV52Dlb4uie/bCU6KDoq4DnrY6MUf+k+3Oh6hSqyGW3Ac4gF6I+kkx3xpSc/w1YaD5afvOOjQi59\nSofDfeKmr/ueO5MVa/fMPhzdz7Gu2Qr0tkcFbtf2h98dcGeH4AqWveMJGXiyiq3kQeJMDvGFXhpX\na222yEsmPwtXJOM30NhYeNeyj+xeCTjaogv+87PJQHSm4/DkE8g8v43GCtv0EX5eSjjEfXjGb0Yb\n8+0RjugAPy/fyX8FScU88aJ749lTdpWsGif/Qk/2nz+AL74qktoHmtmbhiZgsOHWY+vpGZvL9rJ5\n6AmOPeKJvZEhfEHzr371q4ffJitaNI/e815ftEJz/kLR1d/3J9N8GdnzUk88g7fmdxwLrH/ACW59\nd3kmB63ROnM/9bUmeb1c/lMhR7xG9uzBC0Uvk9GvOcFprnPPZt/j9d1QIF7mD8lfx21XwLvJP3y7\nLe/m/Nusf9t1JuybrPk68Odan+X1uX3u+2Jb2Tj20tf17D8fx5azx15o8Se9nBJLs4MaWQLP0Vo7\n/M9y//+ttR9M4RXBMHIyMebuzOZUJUScX1/6ECI/S5Esc6QMC2FxNn8G+60xBae+ec5gTby6Bldw\nQ1hP68scybBACG4cONwcnLexcy33jtZq785wN7bx+t62Nuli79GCcRD4CUYEOXgqyENvfNfP2Quc\nJTb4g86zBWv27evNZ4/Xt6fApPGkrX4BrqKqt/uKCIJPxSaFE4Emvip8+Om0QE7SKtgFx/xgB7d7\nsmGuYpegD1xrea5P4EdHFSUUiRRHBIvuyYr/edIXr/RZUG5ea5Az17ss3YYy04bNeWBzfs/WfwLk\nP4gqAfz+979//EH2t6XwGk3Qg82UDCs8+trKT8bxUsMHBx7TbwGCBE5w7yzBkljFL3xEe23yTx8Z\nkTxlrwUgEl9Jl+TNs4qYyRm4mqRO4sfmkBnJnUPiL2DhC8gWPNl08qbw5rAOGcRrPsocMgh/+zFP\nAukLR4VX+Eh0fenjP3OSnBmjwcuBZgIqZ/vqQM9wh4v1KiSQc32O/JHn7j0zTj/8wWNr+V0H2uCJ\n/SiG+lJH4TF8nONVfe7DZT6zD+viG/4J/PoqCZ2tD7ZEXrEEv/AvnVdcxQO8AIOPoO/6wKvw2n6i\nWziwLwoD3vzbGxh4iA7kQeGArZBkkzN/DgJc8oc3eCXxN5a84D1ZYG/wyrr4D74kW8xivxcrwX76\n9OlhdxTo4WNfeKZoir4Sc3GN+MZPwxSd9BsX/vbjPr7hHRh4pt+x0x1f9dfif7CM77l13M/1Jrxg\ndPbsuufB6XlzOluj1pj6uj+HOxrTCfICb/RzoIP7YLSn1tAf3HN9njd3juu6+WhPR/3nWXhK5sgM\n20WP8Q9+fpqvaETfFXTAgZP5roMLF/aBfvGB5IYckBtfo4DDFihkTVtgXvi63hv455637nxGF8g1\neaXvZJp9sje4o7l5kx+u9bVO8II/8fHsXH9jgiMesHd+kg4p5rHP9NvLWn8iqL/VSffQaMozeGhM\nHxTW8MQvbui8Iho58Zw+i0P8Z0kK5OyIBg/w2tvR+fyf6/YHnvFae2y+e/Pav3s6bY90nJ2TO7BL\n4OAv3MQxxb9sCxvnvpd+bJax59Z5ju4Bj/3kVz/55JOjAEvOJk7hTpdOK7fxUpPtk5Tn46zPTmr2\nYY7cB85k1ksiBTiyQ47YJDB8SezPgSnew58MmavxX+QMb9lS9AADLHaVT/By3McV7LCCANjWMI6v\nJCtkk30WO1kH/vCGb/Sxnj1bG+3FGxX00Z6skC/FWjLPt/BDfDe5oRd8rZhV7FjsCqZ2Ti7id/Sy\nTy8Anq3YzwcGcObjfXFNx/G1sXgaTPDjl+s30djN2i7H8HBMHPhB9Fd4ZavoITvn1xx0k2zWolH3\nznNvs//x+u4osNP9VWgejMn728IJxm13dtt1JvxszOzbr8F/nTV2eJ/F/U7bc/thY8WjPjZgz/g/\nNpONdNBVubS55+yOfXnWWufW+Cz2/t9c894XXifzduXAUId+4zh2zo5zJTQcLufs6E1n8ArECY55\nHCln4vlcpzUwrblTkPY+8AQRHAvHKADhoDl1wbPggRMS0F2ttwoCBjBax/yO1vG8g3Pv+r8pSJ+n\ntaLLzhNBE6PgDTx646NAD72dFUIEdt/85jePL07iydwb2u5trrc/e7y/PQUmjSdt9eOTwut//vOf\no7BGnwW3ElFG3xjJEd2WgHAM9FdLb8F0GNtadK+ES5+x9F2foLkCDBmSEIMrASmR/cpXvvKFi1UI\n8UVECWW4g+Oa3r5qg094Txj0XULpi1eFV04Qjr549SWF5IG9ma091xee3d+H89zDxB+tfcEiISEj\nl6v4zpainznGogebq9BBbiRXEh+JWYV3YzuSBbDzA2SKrVbYknT52uRq2WsyIZnzIif5cVYYY/Md\nrtn+invkltywSwIYz8mV9a0Hlj1YR1JHvq1hL2QT7n2xVbIlsfNFuJcTdMbLCUmZJEayDb750QSO\nZMnZfjXX0cC98Q664sgP9cxY/cbU55zsOkvSyajmbF+KSpItcox+4ISDc3hYb+JzAFn/6Jfko+Fp\nJfp0FX/pAfpVgGQT0NA+8dkYhyARP9EE/u7RCD/IREUCc/APXGPDRzyB/+B7Bgdz0eFqyYTCGXsB\nLwmxRB6f7EvcIREXj+Az/4SfDriRB2uRM8m1n9362kyf4pn/7Vaxh0yDh6YK3BUfFDF8bWgduLBd\ns1ASDdEVvuCChQ5orx8Ne66ffTMWzYxx7XktnjnfddvXehl845sTjvgW3q7tlx7hr2v7ojMO9Lxu\nH+DO1lpolMy2ZudkZt6Dge8Xy3/wI75MR2MvCxSE2DE+j+x5uedvA7Pr4hVwHOBqcCXf/KLkSNEV\nDMV/8MiUQiM4bIEiqAJULbzct7/6Wqtn7dd9z2Yf+0if4eE5e2ef8KYnE/68DpY96e/eOtpcw7Pm\neua+M7mlmwpi/AH7Qke9tMJr+0cLX6D7Ahhu8WdfA13BYqsUHb3UIx+SUPpmLfzx4v6DDz44/Ao7\nQg4cZMIR3HM419ceptzFX2P0z7HG25OiKzvq5ZLCIzmGExtib+iP309WIZDPYePgyHYqzJL9Gpgd\n8O6aXPEtfnHk767yteTNc+OyDdYla4qlfA97Zk0vkuJ/fIInWQGLvfISTnFb/GY9eOEP/VB05bPp\nK1tlbjaPjSVr+AQW38JWsq9snrX90oDss8NkkHziKflAP/jD2RfnXmSGN3mJf/aKB3jOZvPH1iBn\n7Lj1jRdXWIfdR2eyYpyPAtAQTvw2G25NPIjOk7/o1H38B8t6/pyWYiU6KbiyHeQa7e2P7GlTl4J1\nPLjDf+Cu7fD3PbWkfmMd+EavfEWNF/xn/6lQReTmObdWffua9T+e3xwF4t9tVtj5Zu6b4t2+1uus\nM23xdfsF/3XWuA7uXfTvtLgJzOv2oibFxokl+T52mL0X0/I1/Mmc29qz7ybrP+QxD67wWoCSw+Es\nKY1ElVOUIAliOT5JrARFYsaBExBCJBAVHBAgjaAZw7mVnE5h6vo6QSFw8OAIBT8SRGdCytHDlROH\nI4F2hBfnrOU4wZqHtTMKzg59L8PpOlwfQv9U8EkHARy+SqQFou4FfAI09O6LBYVXX4PMAkUwJ7xo\n1bPuH8+vR4FJ4522+EV3/YRMkiGZZPTjpwREAK5QUdIMxoR5U+zMc3AoinS+pGMTBPVsh6CdDEkE\n/CRK0b5E1nqtSyddZ5tuuv4cFwxwatZgjyTXkiB/aoCtYlM++uij//2Sgt2ZbafFhDnHfZ6v5x6i\nM3zZ0RJDf89Soq3PGDTEL8mUn+v6+kVQXxLneQ1888gQmWObyRbb7CzpJmPkwMGG8CFglIB6ccPW\nsDklvyXAs9jKzpR0WT++8jfWkpSAT+7cl4yCRSbth9wp2JoLB/uWlNERePnbkAqvkkl45U/mflvb\necpqdEHDF8mKcefG8G9zHtjhqd9+FAb9JLlCMZwFdejN94IRTuY6whNP0VeyKWGTYNNTusA+oJ9C\nBD5qCp90lk6bAy5eojE+47u+YgH6IyawhkKsIgC+Sq75Z3wx33qSbDxhkzwjG/1dVXKAT+hPruBk\n3xJ3tIMXmXQojhTA2ju8FF795yO+ELZ/ib1ChCKctdCJXJIRsK0NNt9mT+DEo+h3EGT8Y89kkc1T\nLDCulwjkCw3sW4OT9dDVmOTfGo7r1hjL/b9LsmBOMuT6HIyemzyfz+sAG1vzvHvX5KYCEblxzZ6i\no5ex8TSaTThddyYv5AQMskKmyW80Mc4Y64aHs4bWikH+bISzr1A948cU7f3JHMVzduJLX/rScZCV\n4tMDyPoHf/EB7mLc9Ikc4D/cFGXYPl9kKvawfegw6RJ+9YEPNpkFh30jJ/yMPTWueca7Nsf+Hcai\nT+M9d7g3Dozo47rnroODD8aABYda4zvrpw/ifT8zV9BRxKOf9kp/FUh99cgOsMVwMx/8WjjoI+cK\nmr50FXughS84zcs3oC/a+hM/XgbD097xBO7WplNop1lvb9Y01p7ha5/kyVn/3Le5xuMJO9fP8skK\n+aOv8gwHe+WQe7BBZG6HBb4Grx23eKAwSiYVyBRIzTHWntg19s9BVtkwNop9ol/oaCycNXPRxocv\nim18FdvGXhtjrD34KpnvwjNyD449s2vsm/n8HX4rBqC5+XgE1v+wd2erliRVA8d9lF1vcvDeqZ2H\nxkZR20YU8VIEX8BLpVUUcbgQRfDSC6EepR7ly19W/e31Bbn3GepUd3X1DsjKzMiIFWuKNWWeXcbB\nn6zgcTqddtzA9oytdEYD3O0Na/IP+KSlF+CCZW+b10stePBVZMZ+miseRD8Y5hiPPkVlMQr6+GW/\n8Upf2s/Rbt157V4jW7zyn8vy8ejykl0Mmn8HO5yfz/rgRar+9VljHuN8hPMK15ipb2SXf1MAJwc/\nocPeiW/o1oTrejb0Xtvjc4CM4jWdeVk+B2ti+rIwJ6x5Pdd6VWvM9T7q6+jt/BB84lPnIxhsGdvK\nx/Ih7Kjz0Ry4ODzreTpVX/1Ha72pfR/7wmvGO0eSoCkFJ8ygc8wKrr52kVzp57g5SAadw+N0zaVU\ngm8N7AInyQWnOguvxl9qKRbHKNgRNEjKrAk2B1piDSeHdawZXeBfWid6Ozf+0pxLOL8Jz9rIneMN\nHREM47/kkcwFUGRAXxgQX0D4qQEJyrXw+tFow9TdZAgTe4K87GlfnPgTMgmmpKh9S472qCM4nck/\nXQCv/el69rufjX2QRAimFbsk5ootzvRJAiZh9uWZpNl48MCHs+uXDVrAmfiGHzsmaVF49Z9rlVy+\n++67+3+u5WsT9mc2+Mw2eTz7X+frScPEn+18tn3ZJzn2daBEx/5OHuyvgF4iJ+mWLJMNfZFAS8DI\nlW3GS8mUpEoRROIkwfXcM8GHgwzoX4kdW1+CSzfoTDaH3ZFI8z3sDf2xvgZ3sOFRMm99hy9c4Ei3\nJNKnLXlUBFQ4mYEPWPCRFCsSOOOPF0m+iJGYoXnyLF7Ovq57Br/6XNfOPa9/nXPUj3f8nqTeF0oK\nRQ77nL9GP9prwQTLtaAP7xU2FTbwVD8ZkucsfOCZL0XxgWzMwXeyVQS1pmtrmgsGfmpkxieQr7kK\nNuZbH9/J4bTJRb+xaEKPn0Vxhi/cnNkx/h5udMM+ZUMUFxSGpl7AAV6Krv6TGck+nlnbWhJ7cNhB\nZzaQXqIL37JBaMAX97Ppc2h0U4FGQZreoouup99wxWOw2UCFDWe8sqaWjDuD0bV1XHe/T3jJf8J9\ngjkHHy7ZUrQqxOAhmskMreihd2iLpmAHF5yaOIIMFPwdeMNOeDFHdmTQvHB1tr6iFDukuOCsGErH\nrCtu9afx/VSFfe9rV4V5ONM7DSz2wp5RgFK48BXf/GJQIcNLJr+NqdjDPrEd7Ik28QvH+vFEMbd9\nSAfwjS0LB2PXeWCucOcYc7TGdG2MQz/+sYn0Gp8V/umfdRtDnsmD7uM7u2+v8I1kYT/htb1fUY2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mPpUefm9/yTcH4jCq+Ez+BziJyaH39XlOEwGX1ftnJOfZkg0OZUBRiCXU6SUzeekxboCuzA\n1FIo15Qk5RI0OASggg/BCEfrmmPmMMEWDErqC2A4c0FOcCkmuClsCmk9rX7Xra3P2s6OnsE7uHvn\na/RPeEJpXofiY+E9YU9eCoQkNZJwY8ibbCRL5KHf13AKr4KiAmX4xfcJL7znevVdzw/nwOTx5K39\nWJIiKfMVCrkJQgX+7EB72d4zlwxL1BRC7D3P7H/P17X0Odr7qLDPBPS+7hDgCyDBEDCCJyiUgBwV\nXuGkBXe/ecA/4QnObIJbX4EIWn0JIclhe374wx/uf1KfHs85roPneoWp73Vv5/DXXzBA3goSvqLw\n1ZLElY32Z90SJkUQ49lMNkByIzFkE+iWuRI48CRl5C5BKkFk68md3ZBY1MKhe/AddEZCopiraGI9\n+sxv0Tdr0Fd2ij57Wci3eKYAQ9/4JuMUQfgZSSf95D/083d0mx/0haRiBNwUCxT4JLb0FU3g2j+S\nYLjBkS7gU0mgMfrzV+7xFb96QYkmRScN3A5jwQMLbnAEx3z7FE3G8sd4Cy+wJLn4Ijn1HD/NCwf3\ns1mjA+54wufjD9uAn/YE2eEpevh+ybtk2mG9aIgOZ3BbrzXmc9e1YgPjjdVc60fHbJ7jyc2WYCoo\nKQBI3tEoBpHAk53CgTMZ1VZ83Ldm+DrjRQcZuNY/5awv/YGjdfCBXWncXK+1Jq3wAre1jbEenuM3\nuYJFnnSi8daocHvEn33g+Ad8LRx6BJfW7nnPjJ3z9M+xjdPXOH3wmX3WcPAl7Abdsgf5BbTaP3SI\n/a34R5Y1slZI86UkP6JYo4k/2W/FGjqpyMA2PdmKQ2QBpn7X9FqBiI2wh9gqdgoM8PFbEcefHyvs\n2gP4T75adKMNLZpreLIZ9h5dU8h1KBSREVsEd18U9rWrYpU14+8KO156ftR6Hp+NMdZ66BXD+81y\nhTv21l8qoMm6ZGCPKObB01eEbAaY7IjxN9u+Yu/grQ+u0Wwteug+vFtfn2dsG/vgxR3YdJjMFM3Z\nfUVs4/ycjf88T1FM8wXjt7/97V034Ik+dDmSQ332Gf/SfyrkBSo52JN06tPbzwz4M3Iw2bGJ677Y\n9s+kqb7O1myOcy2e63NtbxbPKLjSKfkT/0cW8MY/9knhXdHSF4n2QDEyOHC3x/k1sqHTDvaVvDRr\nwhmN7D552hNo5ZvtLbyGe7ixEfInOuHlMr3XwOEf+TR7Co/4OmuRGR9oXvuQPOiG31T1Z+y9JAfH\nWvHSeD5ZfmAPkMuzbY/RCXwon6SPeGBNusCfGSs2tW+Nwyf7mQ+yt/71r3/teKEPvei2p+xrfNYf\nba7B5ffxk66JY9ABbi/rrA93Y53PyXoH/Ar/wUN2ig7hu2s2CR/IGQ/icTjSGb7u6fbSwc9H2Ed0\nX4wmF7Pf+G001+JR987Bm33X61fLAXK48v3V8vgu0Od+OCePOeYSzHPz55wJy57Pr639YHU03xjH\n2t/zT8r5jSi8EiRnKRgV9HpjzJgLKBh7QaxgSeCQkxTwCDA4MoEBR81xgjWdcIqSQnAc+gQcHD2n\n358vWkdwzOFw1OByQgI4RVcBBOdofgruTHEdOSXPjYOHYx2fc2083Ixx4IPzbOv9fPaqrqNvha8f\nTQIYB7rRCm/ygmt0GXfUwDhHU+s2Zo7jxAV55MThkwlHTz4CRgUUQdl3v/vd/72lnvDgAp6jfn3z\n2v21vRoO0BPBMJn5IX77154WhEo4Ja10SOBfMYsOsQterAhe7XOBrHHa1A9yTJbJ2Rj6KEk4nU57\noC2YTG+NhwP7ovAqyGRjgmM+HCZsfZfaql+NPepHB/2VlPhTLcG5goDCK12WGNhn51r0T3zPjX0d\n+5NT+DvHJ4mtwpWA3pcyGlstqPclBVkZT6/YA3pCN9huPgRfFQgliRKHii3shENBQpJYy251Dy49\n5GvoJFgKltahE+byI3RLAklvXOvT4K+wr0jLn4GhOCIJsbbk33iJCr/jC6G+DkF3P7FAP29eFCIU\niuiDAgd/x18q/MBRH1rhAR844osz2hx4y79JbtGCPnjSQ7wE21y4ORQ90ANHewYs482znn7j+Gnr\nKgCgF92eg2tsSbB+MKzlrLkGR5GJTL1g9YINL+xVz/AMXq4l0BJqibkvzdgGsNoLwZxn1zVj48U6\np3s4ad03t7P5xigM+Vqe7IytIE/e8OQXtaM1wcBT8yYv6qdf9APtDmM1Z3Ku33W6an16MYuk+6Rb\n/kGLIzzIUsKMLtf0iBzpTjTpo0PWal78Amteu9f0zX7XnsUH98EyvnnN7T4Y+muezf5khIf2u33k\nQBNdo+v0VXGKftrf5IaXcAALb+m3F/98lK/f6KQ9YI5ijD0IhnEKSL5kU4AiC/tYEcwZr/BMMx/f\n4KxoA65CEF+ooANHDQ216IOXfrDsJ0UisZCiEf8hbiUrtCoaebnUl/3sINuDJ2ubvJt8Th7x0zxj\nJz6u0cPmeonoq0b8Eat5MYF3/BoY9JN9FgfYw2iga/aRIhY/7Pq0+WtwW6d140ky8lxfOOlno/Ff\n4ZetYzvAowdoby6/4vfVFX/tJ4VSf22iqDd9g7XNsZYzP4Pf/lNMMKxnffpiLh+lKKfYTRfYUPPa\nw5PX0RWt7muN80xzb/9ZRx858yv0SxGb7BXN2B5j+DzFSvLHU7pADuw6ntF1jV+iQ3T16VZE81Wm\n/ZC+Rrs1+SDxknicftHXimuTBvz3IoON9tWxv2S0B9gTuZc5/Ia9yK6jyzpwebb5cDRlW+Bqj/iK\n0p/n8w/52Z2A7R9z8cdcBWP+05m+2Qv4AGd7Wd6HDviyaXyIQjWd5FvsZboo38AzugEfv88KJh0S\ng5Cxl27uyRY8rTP+4QP4bIu9iv/wxwN+31h4z/MO5JH+wZf2zAQ519TPX8t333///V0P6K19y6aV\nf08cwWU/7WVfuvqZDdf46neSHc2bMax1Wzs+Tbyu11cOXDnw6jhg792lXffmeS69EYVX5DHiHK8g\nSSDGYTHWHFrJn3sORLAhuODI+3JOoJHjPWIXJRKICJo5PQ6BAxaMcIQcv8KqQKE3xoJqAZagSgJ5\nBB8+nDLYDk0wUxBhjvuU3fgOY3NCrhvruta87j/qc46XLHKm6Is/bVZ4u3ag666t+c7RXp8vB3w5\nIYikH30xIQCWyAiq/V6SL17786Dmtn78nv3zunHX88txYJUdaPRAgchXKL4yEcgK7iSqAlhJgiYA\nFAwLwCW0EgKJgMO+1z/31D7pxT+rLOe9Pc6WONgBwbUgUaIkeVDUq/AVTHSkv/bthNeY9WzO0bij\nfrAFu75uVGCU+ODDO++8s+s621Sytq7j/ojPR+Ne1z74x5d4Fk2CesmQRB5v8EoyxAZI5smNTILh\nOTvAh0hwJDvskqRLkucs6WOv2S72uvn40/psd8VJ+gqWMx8jETRPsQRM+iNxlECBC57kUjKsCKEo\nioaKcHTPnAqV8JBA6iN3hzF9ISNZU4TxdQy66Szbp9jjz/sktMaiGd5wA6+kFD70B25o128+n+Zw\nPWk3zhpsqaMiDTzTQzCtU4EWXyuQ2rvgasZoeAlfOi6x9cWT/auRHZwkoYqYDsUaPLUO/MjQ2taD\nRwUtCb1CQ+ulNzvgF/8k08666Yn7dfxRX7CaP+fogw/ZowFcOktH0Hdko4JzBBdsz+MvWaBZAcg1\nHrQmW1bhH5/xSPzCXjrsA/jcp4EdfXSFbaaX1kEXPjunM85isXx/a4ETrOC516KxseeeTxgrHfiD\nH3QO7XDiE6ZczScTuiTeU3Ck1/rIxR61p+0ve9X+REcyI1f7ToGmwpX1NHPZBIfiovXJSUzJjymg\nWkfhyv785z//+amnWzEr/JzRDZ4io2KrIo69T+/NRRc60dE81+bZY9a2j7yI5EftA7ErHUBnRUyF\nJnDtYzimR+1l8DpaL9k4ezbPyWX2ubaugpQvAv2esWKXtdHVV6t4C2e4Km6JrfFA4QoP+N/2vrif\njpsTXuECB/3h4rzig2f0017U8BM8cgXPfPzqNzfF+mzyl7/85U/95Cc/2fXFeLCty7Y603d23Qcf\nfBJayN84/uhme0Hmi2U02T94nizND17XcAt/17X5XF86A/fu7fP8BHz4GraW/6CzfKWiGR2mV/a0\nvYwuDU3khB6xlSI4GPSWjuFReOAbOdJtMvVbuGIl9JEVGtHB7pgPjqMXoPaY9eAFhtyL/yQjcrA+\nPOCDRnpqP9prcOEf+D9fuprvuXHwc8AVP6wpL+xLZ/DRTyY3m2zYArzAA/Pt376KpQNkyZ+Rn6Kj\nuew7PLwcp9to8YIQPH7Z/kqv8DWcXJNbsayzceRARu1F47UjPdgfvOQ/4MNj8sv1XA//+GiF5V//\n+te7nqNf8RSN8ik4g9U8c/BF/uXFheLrs63oLWb1c1C+WMdH/KYftfgDTrB6dj1fOXDlwKvlQPbm\ntlWue/M8h96YwmvOgZPloDkGxtpRkEgRPBN0MvZPt2DWIfg0T1uVKjgCjr56EpRy/JwCh84ZFoCA\nq/jhLICwHqelWX/Cdw83TpwTzbkYI2gwl3OaMIyHkyOa53nCt+Z6r++jbuhONnBBH3r118J79nlW\nf+PWc+OdjXW4xi+FVwGUAFBAJWAif4UNAZ9ATuH13Xff3QO8grNgtn4wW3s+r+96fjgHVhnHX/2S\nXcUXf54nKJfc+s9pkmt73csXgXSJmqROUC1RttfnGsGHsf6jZ/rad/ar4FpiJIAWjEtSfJ1ChyS+\nE6Y97N78uzRrzfnNWfvZFeMUln2d4csZdEo+33777T3gZbPiSXDmOVqP1pvjXtdr+MeXSYM+SRCZ\nK7wq1JN7hVdfongBk0yaS1aSbYUUxUi2XSFCoXTag3N8Y1cUccxX4FMcAEdhoWIWXyJJpz/0F3zJ\nFFzYQl9Z0W1+RHGUfyrxUciSmNAzCSu8zVXkpIcVXs2XQCoeWwu9vqKSxMCnPeTFo31i3WiiL+jW\nwHffC0xnPMJLtHpurD3RfAkW2vhG9JljDPrAwkcJN7rtIzhVYAATbGPwyRx9dFwRSoFAgq2AYT14\nW68vXdFvbeuRo3H4rlnD+uiVJCs4sQ/Ws05+eh+8/JN+6LZu99F8dK8vmOvzwFtX67nxYMJTn+vW\n8Kxx5uh3X1/jJp/xkD3CTzB7Rl70xhmv8JjNFMfQWbwLd2vdt6Xj5Gt9RQP70ZmvB9sZ76fuRUO0\nWzf6XJ97Xr8xGjod1lnpEHvQObqJB3Tk2Zbwozk4+EKnfK1Gpyogw9n+UTxR8KGX+KVfMx98um0/\nmq/4QieNMVaxhh9jE6ytKUIpVChSiC/RbO/yc76otF+jxxrwVpi0p8U11qmQvAPc/gEj2s0hYzKA\nv9ingqu9gA5yIC9+THHM14gKJviUTwMzuPDRwHbUPJ+t+8Y562u+ddkrf8XiP4j0cpS99Rcb8MB7\neIvbK3Lhv4KWYjXbpkAoriNX+qyFU+t0H/7hFa7uGzPnNx4c1+yflwhk8uc//3kvqLH3bLLfZP3R\nj360y9v44NH79IZN93Wfr4vJhL6w6V4GimOc6QsdXHGceIV356OxcI3+5loTvvgsXvClq0Ij/NhI\nuoSf8JDnKLqzH+DTYfsEPeyFPeAFlvmKn/SbzkW7OXSHr73ZCo1k5SU1fQ9m+IPL9/lw5ekWlzt7\nqWwdcMwR6ymykzu87B8+3j4RA5kfP/k9cjLf+op5CoH0pEY+1uWrrQdGP7OBVj4M3n5L1UsOMkYb\nHqJV4Rzdzu7pn/3rJQAcyZH/UeBWaOeT4WxfKQT3MxLZ+2QUfngJj2wknlnfkbw7z7mzL1gPPeNR\nMj0HA4/ps6KrIiqZs2X4RuZsqL7Zsof2kf1A5uwi+cjDfFDBdrIF+DMbfND4mHRO+NfrKweuHLhy\n4FVx4I0pvGJQDiInwTnVPBO8SbokbpJaAQOnLQjh2GqMOQfH4HOcAnCB0ZOtoMERS3IFoxI+TlUx\np0SOgxXEChI5Z7isbToMOAqwrDcDRvhyTA7JiXvzptMFW7/gSnM923o/n31Y19Mxhk90FGzoj/+u\nGxeORzB6dnQ+Go9vgkhvu8kSXwWeAkbBo3uBHIcvcBYYFfiCB6fOXbf2XK++6/lhHFhlD0r89cz+\ntW8Fdwrm9qCEVZIgYLOPyNL+I1f70p50lqwJxMGZuue+feraMde1fvtOv7mSXEE42+AFjARRwsou\n0Js5H2wwomN/eOEf669jV5xMD2f2RkAveJU84IPCq6CerVphzaWP4M7nr/s1/Ce/Jq3sveKChMeX\nJgqiEqK+XvH1DLlqzQMLX9l1CZkkThGJbTZmjot3zmw0+ApXdE8iR9fYZnPoTAf9UIykQ4oH7o2T\nzJpHngoRfBNfBRfrK4pIYCq89qVkusg3KVKAZ28oVEpcJdOSQLbPfnm2FZkkmP3nWyV1+IAf1lLY\niRf68AAc/RoeoUcypc+hD8/QCyd7QSJqrrHBjnb08LHgWhMefKamD64la/a9l5kONElyJbDGm2sd\n+5ENh4t+z8kETvgLD/KUJHopqrCl+GVduLWfdgQu/LPqQUPTB/eNCSb41nbAFz/y7fE/PYEnvhgH\nb/jTZefGtmbruG99fa0HhsO61tQ8pyMdxlrHGnjjbJ1w3yed+Qes2cIBbHImQ2uTB/22T6xlnGNe\nH8HRN9cI/hw7n+s3Rp91J3x9aKWbvi60j+iZfWrPKcLba8Yo8vgqj42gp/wKvtjbdEfxlP4oVpFj\neFmDzWWDHfyD+XipqGAuG2GddIG8xZniEwUvexzeioy+dnXQeY0MwVQEsp8VX0+n077v4W19RzwA\nh97YL/AFB51iH3aAvSETfAAHzQ5FY/dwJ8dgwsE1epxrPW/d+p31wU0zrzHm4B06/Q63g91j1/pN\nagUzughXL6B8VYePcFKUwTOFQrJU7KZ3l3CBw8QbLrM1d/a5Dme4yBfgrNjnK0a8xGf4+GspxVe4\nRJ+v+vCdPReHKNKhk21ir9hm8vRSUCyxFiXDKRzCLTqiofue609O8KNzFX/FRPhJ7/GTDtBB/CZ/\n8S89ZFPZD3PZYHpPh13THXPJxEHHrGNd8maT7QHycYiPTptOoTm7Dufwggv7rhCHP70kZ8/h40WA\nYh78+Bb95prn5SpZ0G26YV1r0S80o+1mK6CKifhIjU1CA/mJm8SV+GJdemRN+1Hh0Jp4Yf+RGx6Q\nJZkab4+hl72ITmviP97wxeIz/pxNBFch2Bkt7d1k17n9kuzbRz1fZW68to5r/EPO1m791nNf4zPE\nWWIs//kXfpOVl0JsFD6Sef6nefjPFnoR4aMBL1XpkD3gy2Tz2Wn2IHrW9bsP5vV85cCVA1cOvO4c\neGMKrzkChnheEwBnJAlTiOEoveUV/CjE6GfUBcEOAYGD0+S4OfC+JOJ4BcngFYRz2BwHuAIATqjk\nKKfQGS45MNeatUuOChqfP3n+kwMcOgdlTXAcOSF9DjAnzV0H58M+T3q7Difn+iQzJdd4VmImWEKX\nNsd3vz+48E/w1/HkJzASBAgQBZECLeu6FzgJhn7605/ub63JY8IAFz4rTnO9C2hdH93CAXw91+Kx\nwExRSRCraG5/nLZ9KrhzFozrk2QKqB2u7cvgCwDtITDJne6t+wgenjvYBbrq0Ix1LbmSBPvCQUDf\n1yH2czri3HU07EDu+U+4Txj64C8B8GfYvl6UTMFH4VXyKqi/1I7gXhr/uj2DvyNZTfwkZSXIvqaS\nLEpyfTUjqBfgk20tXsz7I1uL53SAnaJrCjcSOHr2bCsQSMr4FXpAR8jA1zdsXTYlfXMGQ7Liax36\nyk/1kgAMRTg2S1IsEfEVs+SxoqU11iIuf6Swys/xH2yfPYJP9EWSKbll+zR04gX/BpZ9FI7mwINf\ndNbsIQmusdaXHOEfXuA7eGA45n5zrU9Bx1wwwYejuXhqDbYYXGPh5ZkEV2LNbuO3pA2fzQEXLPx1\nr1+CjD6wycwzsOx3hQbjpq9e5b8TevAPfNE3x7ue96YZBy8H/qDZUSKJpgoWcAFTAUzBg3zDFS1o\ndZgTztYwR1vXr9/a8NA6G+u5I1rIGo863N+lme8As3Pw4d/67Rlnz7XGu5599dfn+Wyeaz2P1tln\njLXRMfWYDijSKY5UVLX/7NuKPeadNl/iuSIY+OI9+kdezhX0wbZWONFBfoAN9iKEPOka+GINuuue\n7oNtX7IL9P3J9mJf8U6/PSSu9B8mKki0T+EkRvn09pME1mBbojX68SXdBoctURix5xW10IBmdNEz\ndkEBF0/EunCio3wc2MbFa2tEqz6H+/o8n81zPAJjjqEHaBIz+/NkhTP2k21TjPICXDESHnimIMfv\nK2LqgzP67RV2glytUZv4dh2e89747psbnvod4KIBvgqNbKsiMfvq3jNyhrfCqy/12C00slN4b7wv\nKdl39ghv4e6F8c1WEFSYRDtd0MDUwtk9XLrfH7543rVzODsX19CBioXWpwe+0lRAZu97eazwy794\ncUA/7YP8G7srbyID+sPHmY8WuhZ+eGXf00tFN7SB2wuIeGkevNgzftneEM/5ahKO8OcH2EtyFsuA\nx3+HG3rxWExIFg57Ew9n4ZX+kEc/B2U++NbFC0VRL675X/jZiwqHXtAqutI1vICvOXSWDsj98MCe\nt958CaDPeLwC28tfa6Ebj/0ZPR33IpgfB1+DV42su08ne9a55+7vMr559z2Tb7pnTYd7ewIf/P8C\naPRiykcIviwms9NmR+3X4obwdKY39geZe4lhf/B1ZPyFL3xh1x02tBc/rW9udOu7tisHrhy4cuDj\nxIE3ovCaI3DmOLUMsj7OTrLJsPvSRyAkEBEscewCXYkth81Bc7z6Coz7+kTArIDDWQikwQFPMCKA\nmOsKCLSSD9dw0e9c43A5Jfgaq0WDa7gLoOY8Y8GYh7GvU4v/E6dJN1qfvEg0BPocO6ct+JEokM9K\nH5gOYy+1de3m4DP5kjMYnDy5eW4tzyQ0Cq+CB8FTsDxvXNfh0Jjur+eHcQBfz7V4r0BlvwnUJDN0\nxR6yRyVgEgl7RUDYn3ebS7Z0jtwFfBIKgXHJydSp5OlMZyowTdhsgeSJjZC0KuZJFNkR66Qjzl0H\n9xyN5/pXvkw4aBD4+trj6fanWpIMeHzzm9/cvwRm3y61YE+Yl8a/bs8u8ZeMfWGiSO+3A9lshQuJ\nscRHsithryUn9/TBfYkiebMX9EoRhl3mCyTeFWQko5JA4zS8P22JBx2RiJYwmsvumO+g0+b6Kkpi\nRxZslJd/knOHpNy9Qgm49F1Cw0bR0Zm4wV0Bnk74yw44ogMOcJc4SqTxJxrBAddhr+i3TypW0un2\njz0DHv+poBxOnoOPR/agMQ7XcGqPwRV99o9n+IUHklhrWp8PLmGDCz3HK3uab8A/RRjzzMdT47T2\n9H6z/YOf6bcxjev5fc9omvCaH32ea/hBTvij8EAXyJGdgkNf+yiCoANfKpDjqzXoHZ6im26Yk90C\n4wiP8HlV59YEf167X/nruT48iT/GaZ4135hafd3Ps2eT/z0zH/zgBKP7zvaKFxgKKv5S4rTJBDx7\nhB31ooZesfW9LAFXbGLfKA7Sb/CDmbz12aOKp4orZG8Mm6CwJAYlS3oBNjwUuegDH0Pf7T/X5Cy2\nVCg13xx74p3tt7v950DsWPGJNcLHPrAGfYIz26RIpHCpsEyP4As3BWhFDrFOvEB3vMPbruOt++j1\nPB64rjWne2PMb55re5ht9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5V4C1q8oZZACdjQhTfgHdF9HwrDY50zeeeZIEmC4zeGJAUC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3Mgve0M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\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+fXPubXAgOw2cPSy6h0eKXhXq8MILMYVXOsA/1MKx+z28+k/nm3HgmHz1dUy/QY58Djn5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\nntlj4J144Dql56OBW3/x2sShLk7E8QSxPknzdQa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Ph8Mew0xfu4qz8KI17V0dH3iYacLO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\nCUe0Jv67WrdI0IM85VeXZnvPa1v6gktQ5eLqm9/85h4cCrpmgnfS0xe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4oHd6I5PLVl/Z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i8GN8/wJ+GH/vm59d/Fq285icvsC3SMTinclcZK87k27XjAP/2JL1xk40PdZTZeJHOBr4tbnDu8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Recr+hGNkUz7SpTeetrbwVZZ8tXRgj2rOO+Z/nBDmZ5PjtDPnplnjPSQRdzjmOcQ8hYCZMm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SNJhvOGh/MuSY9cwPR+WEGHfp4mDM+1nOfdes95D3cKZkeJ3E618EHyFO4bBr7SQh+rnQ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\nfrY70x7qPvyP2peHbnQim7s2iF2clHiweYE+5i0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jZ4U2T62fj97xGkHnhDLxhJmGsXO8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eb8Uq7TjrVTrB3XbFvefUJ9C466eNAcsOnqtBhnqX6az5yBTpk5DUbG2EDUT/QXgrc9fPRs4W/j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pL+PtRZM+0egmBV6nB6JFanzmFK/oOM/6JGMifkvxtjI0c4CDE9ErolcfyuJFONWhEx1oBW9e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pyTpMlXqNziJm/it6D6lNDXnvxougXZpnn20d4GzSLrEGUBk1qkbT7lbTRN6hY/G1cbhrmx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nHFHwhXiO7+rBUx1Kj83bxcOb0uc+97mbQkQZavxURxgv8TbLrkc8+oV7HuVrKxlSEimD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\nUldTeGKPF/rIqjjJS5Ov9GA1BK/C4CdW+GbPp+3VHtqkz/kf+tjdlVd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8smuTkLEAzPWz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\nAQIECBAgMI7AP98nTj9tSSMZAAAAAElFTkSuQmCC\n"
+        }
+      },
+      "cell_type": "markdown",
+      "id": "f9abeffa-c9c8-4358-8595-dc56aed105d9",
+      "metadata": {},
+      "source": [
+        "# Recursion\n",
+        "\n",
+        "## Outline\n",
+        "\n",
+        "-   Call Stack\n",
+        "\n",
+        "-   Recursion\n",
+        "\n",
+        "-   Time and Space complexity of Recursion\n",
+        "\n",
+        "-   Mergesort\n",
+        "\n",
+        "-   Multiple recursive\n",
+        "\n",
+        "# Call Stack\n",
+        "\n",
+        "## How a Call Stack Works\n",
+        "\n",
+        "-   Your computer internally uses a call stack (stack ADT) to execute\n",
+        "    functions\n",
+        "\n",
+        "-   When you run your Python file, the `main` functions is called.\n",
+        "    `main` is pushed onto the stack\n",
+        "\n",
+        "    -   Sounds familiar? `if __name__ == \"__main__\":`\n",
+        "\n",
+        "-   As the main function executes, it may call other functions, each\n",
+        "    functions is pushed to the top of the stack\n",
+        "\n",
+        "    -   The currently executing function is at the top of the stack\n",
+        "\n",
+        "-   When each function is executed, it is popped from the stack\n",
+        "\n",
+        "-   The function may return a value, which is passes to the calling\n",
+        "    function (the function below in the stack).\n",
+        "\n",
+        "-   The calling function can use the return value and continue execution\n",
+        "    until the stack is empty\n",
+        "\n",
+        "## Basic Example\n",
+        "\n",
+        "-   If I run `round(float(\"20.24\"))`, I expect `20`\n",
+        "\n",
+        "    -   The `round` function is first to be called, it is pushed on the\n",
+        "        call stack\n",
+        "\n",
+        "    -   Then, `float(\"20.24\")` is called and pushed on the call stack\n",
+        "\n",
+        "-   Now, we pop each function off the call stack.\n",
+        "\n",
+        "    -   `float(\"20.24\")` returns `20.24`\n",
+        "\n",
+        "    -   `round` uses the return value of the previous function, `20.24`.\n",
+        "        It executes `round(20.24)`, which returns `20`\n",
+        "\n",
+        "    -   The stack is empty, so the program finishes\n",
+        "\n",
+        "# Recursion\n",
+        "\n",
+        "## Motivating Example\n",
+        "\n",
+        "-   Suppose you are looking for a key in a box, but the box contains\n",
+        "    more boxes!\n",
+        "\n",
+        "![](attachment:images/box_recursion.png)\n",
+        "\n",
+        "-   2 minutes: write down the steps of the algorithm you would take to\n",
+        "    search for the key\n",
+        "\n",
+        "## Algorithm 1: Loop\n",
+        "\n",
+        "1.  Make a pile of all the boxes\n",
+        "\n",
+        "2.  Grab a box and open it\n",
+        "\n",
+        "3.  If it contains a box, append it to your pile of boxes\n",
+        "\n",
+        "4.  If it contains the key, you’re done!\n",
+        "\n",
+        "5.  Repeat\n",
+        "\n",
+        "## Algorithm 2: Recursion\n",
+        "\n",
+        "1.  Grab a box and open it\n",
+        "\n",
+        "2.  If it contains a box, repeat step 1\n",
+        "\n",
+        "3.  If it contains the key, you’re done!"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ccd09003",
+      "metadata": {},
+      "source": [
+        "*Which algorithm do you like more?*\n",
+        "\n",
+        "-   Notice the function is recursive because it calls itself\n",
+        "\n",
+        "-   Both algorithms achieve the same thing, but recursion is clearer (to\n",
+        "    me)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "bfe1c5ab",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Write your answer here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "58070806",
+      "metadata": {},
+      "source": [
+        "\n",
+        "## Formula to write a recursive function\n",
+        "\n",
+        "-   Since recursive functions call themselves, its easy to write an\n",
+        "    infinite loop\n",
+        "\n",
+        "-   Let’s write a function that does a countdown"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "1b65eaad",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def countdown(i):\n",
+        "  print(i)\n",
+        "  countdown(i - 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "35134324-9ff9-4c18-8ab1-531d7b6782d6",
+      "metadata": {},
+      "source": [
+        "-   This runs forever, so we need a *base case* to tell the code when to\n",
+        "    stop"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "1cf5d588",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def countdown(i):\n",
+        "  print(i)\n",
+        "  if i <= 0:\n",
+        "    return \n",
+        "  else:\n",
+        "    countdown(i - 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9b037a8c-5cbf-4b11-80ce-82f847958a9b",
+      "metadata": {},
+      "source": [
+        "## Factorial\n",
+        "\n",
+        "-   The *factorial* is the product of all positive integers less than or\n",
+        "    equal to the given integer\n",
+        "\n",
+        "    -   $5! = 5 \\times 4 \\times 3 \\times 2 \\times 1 = 120$\n",
+        "\n",
+        "    -   We define $1! = 1$\n",
+        "\n",
+        "-   Let’s use recursion to calculate factorials"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "b2b9ea9b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def factorial(n):\n",
+        "  if x == 1:\n",
+        "    return 1\n",
+        "  else:\n",
+        "    return x * factorial(x - 1)"
+      ]
+    },
+    {
+      "attachments": {
+        "images/rec_call_1.png": {
+          "image/png": 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xuE8C+Z1AkP8gxiYhMALyKlIoqGhu/BLNCC/TeNGNJnB8YCWyp8v2NuF8tBfrINdlW/aY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\nBvxmKSRAAgWIgNeoOFnzTI2iRHMwZFo43uKihjFPEk4A4OQBaewHjh5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BjQACCCCAgFGA\nZGqEIwwBBBBAAAEVIJmqBDUCCCCAAAJGAZKpEY4wBBBAAAEEVIBkqhLUCCCAAAIIGAVIpkY4whBA\nAAEEEFCBZm2U1sViUR4+elT6Fm0EEKiBQPHoKOz18ZMnkkgkwp/rsbH/9asf1otMRt6+e1ePQzxz\nY8rn836dMmtr8n5n58ytXz2u0Je9PT+snRLvyGR6FHyAnz1/Xo/rwJgQ+G8FlldXG2bdX25tiXtR\n/p3A5qtX4l6UfyeQzWbDziKTaXNzs6RSqXAmGgggUBuB4s+f8jSd9p2PXrki51paajOQmL0uLy/L\nwcGBDA0NSVdXV8woZqtG4M3r13JYKEh3d7ckk8lqFkVsTIHV4B/b/f196ezsDCMik2lL8IFdXFwM\nZ6KBAAK1EcjlctLR0eE7n3vwQNrb22szkJi99vX1yebmpkxNTcn4+HjMKGZDoLEEhoeHZX19XXp6\nesKBcwJSSEEDAQQQQAABmwDJ1OZGFAIIIIAAAqEAyTSkoIEAAggggIBN4BfTPIXA4FYHUAAAAABJ\nRU5ErkJggg==\n"
+        },
+        "images/rec_call_2.png": {
+          "image/png": 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WTAJ+EPjuu+9k0qRJqvmnP/3Jjxz/p8Ie6f+x4BUJkEAYCWDo9tlnn9W9el26dJFLly7p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\nIICATeA3Zyhxs9Qtb74AAAAASUVORK5CYII=\n"
+        },
+        "images/rec_call_3.png": {
+          "image/png": 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QJwQGDx4s1lo+rPXDOj8KCaQHAjSm6eEp8h5IIA4ITJkyRSZNmqSeu0OGDNEWY/4UyxMo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\nAABJRU5ErkJggg==\n"
+        }
+      },
+      "cell_type": "markdown",
+      "id": "55a43d42-3613-44d5-a622-898e39f4af52",
+      "metadata": {},
+      "source": [
+        "-   Let’s examine the call stack when we call `factorial(3)`\n",
+        "\n",
+        "## Recursion and the Call Stack\n",
+        "\n",
+        "![](attachment:images/rec_call_1.png)\n",
+        "\n",
+        "## Recursion and the Call Stack\n",
+        "\n",
+        "![](attachment:images/rec_call_2.png)\n",
+        "\n",
+        "## Recursion and the Call Stack\n",
+        "\n",
+        "![](attachment:images/rec_call_3.png)\n",
+        "\n",
+        "## Multiple Recursive Calls: Fibonacci Sequence\n",
+        "\n",
+        "-   In calculating the factorial, each recursion only calls itself once.\n",
+        "    This doesn’t have to be the case\n",
+        "\n",
+        "-   The Fibonacci Sequence is a sequence of numbers where the first two\n",
+        "    numbers are 0 and 1, with each subsequent number being being the sum\n",
+        "    of the previous two numbers in the sequence.\n",
+        "\n",
+        "    -   Notice how the problem is defined recursively"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "4dd0e144",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def fib(n):\n",
+        "  if n <= 1:\n",
+        "    return n\n",
+        "  else:\n",
+        "    return fib(n - 1) + fib(n - 2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5daa1db5-0554-4389-8138-ab87872bc79e",
+      "metadata": {},
+      "source": [
+        "## 2 minutes: what is its time and space complexity?\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "07b4591d",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your answer here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "ba524b85",
+      "metadata": {},
+      "source": [
+        "\n",
+        "# Time and Space Complexity of Recursion\n",
+        "\n",
+        "## Time Complexity of Recursion\n",
+        "\n",
+        "-   Generally, recursion doesn’t have performance benefits compared to\n",
+        "    loops (in problems like finding a key in nested boxes)\n",
+        "\n",
+        "    -   However, it is simpler to understand\n",
+        "\n",
+        "-   The time complexity of recursion depends on the number of time the\n",
+        "    function calls itself (branches)\n",
+        "\n",
+        "    -   Factorial: the `fact` is called $n$ times before reaching the\n",
+        "        base case so its $O(1^n) = O(n)$\n",
+        "\n",
+        "    -   If a recursive function called itself twice, then its $(2^n)$\n",
+        "\n",
+        "-   When a recursive function makes multiple calls, the run time will\n",
+        "    often be $O(branches^{depth})$\n",
+        "\n",
+        "## Tricky Example"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "ea244039",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def recursive(n):\n",
+        "  for i in range(n):\n",
+        "    # Something happens\n",
+        "    i += 2\n",
+        "  if n <= 0:\n",
+        "    return 1\n",
+        "  else:\n",
+        "    return 1 + recursive(n - 3)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1deffe46-9bd9-436c-adea-abc5d6db88f2",
+      "metadata": {},
+      "source": [
+        "-   Loop takes $n/2$ steps, because we increase `i` by 2\n",
+        "\n",
+        "-   Recursion takes $n/3$ steps **and** the loop is called recursively.\n",
+        "\n",
+        "    -   In other words, for each recursion, run the loop.\n",
+        "\n",
+        "-   The time complexity is $n/2 \\times n/3 = \\frac{n^2}{6} = O(n^2)$\n",
+        "\n",
+        "## Space complexity of recursion\n",
+        "\n",
+        "-   Notice the call stack takes up space in memory. How much depends on\n",
+        "    the depth of the recursion\n",
+        "\n",
+        "-   Think about the maximum amount of space the call stack will need\n",
+        "\n",
+        "    -   Factorial: $O(n)$, when recursion reaches the base case\n",
+        "\n",
+        "-   Even when you have multiple branches, it’s possible only 1 branch at\n",
+        "    depth $n$ is in memory at a time\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0712a264",
+      "metadata": {},
+      "source": [
+        "## 2 minutes: to find the key in nested boxes, what is the memory complexity of the recursive approach versus the loop approach?\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "2906e615",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your answer here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e3095511",
+      "metadata": {},
+      "source": [
+        "\n",
+        "## Live Coding\n",
+        "\n",
+        "Given an list of positive integers and an integer x, we want to find all\n",
+        "unique combinations in the list where the sum is equal to x. A number in\n",
+        "the list can be used multiple times.\n",
+        "\n",
+        "### Example"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "b31da01e",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# INPUT\n",
+        "lst = [1,2,5,6]\n",
+        "x = 6\n",
+        "# OUTPUT\n",
+        "[1, 5]\n",
+        "[6]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "bd660dc1",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "b17e5971-fb5c-48a4-a92e-87a720633a26",
+      "metadata": {},
+      "source": [
+        "# Mergesort\n",
+        "\n",
+        "## Divide and Conquer Algorithms\n",
+        "\n",
+        "-   Divide and Conquer (D&C) is a general method to solve problems\n",
+        "    utilizing recursion.\n",
+        "\n",
+        "    -   Figure out the simplest case and use it as the base case\n",
+        "\n",
+        "    -   Figure out how to reduce your problem to the base case\n",
+        "\n",
+        "-   Let’s start with a trivial example: how would you sum a list of\n",
+        "    integers?\n",
+        "\n",
+        "    -   Solution is obvious with a loop\n",
+        "\n",
+        "    -   Let’s do it recursively\n",
+        "\n",
+        "## Divide and Conquer Algorithms\n",
+        "\n",
+        "Step 1\n",
+        "\n",
+        "-   What is the simplest array to sum?\n",
+        "\n",
+        "-   Arrays with no elements or 1 element\n",
+        "\n",
+        "    -   sum of `[]` is 0, sum of `[8]` is 8\n",
+        "\n",
+        "Step 2\n",
+        "\n",
+        "-   How can we reduce all arrays to empty array?\n",
+        "\n",
+        "-   Notice `sum[2, 4, 5]` = 2 + `sum[4, 5]`, but the second version\n",
+        "    reduced the problem\n",
+        "\n",
+        "## Divide and Conquer Algorithms"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "62e23418",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def rec_sum(lst):\n",
+        "  if not lst:\n",
+        "    return 0\n",
+        "  else:\n",
+        "    return lst[0] + rec_sum(lst[1:])"
+      ]
+    },
+    {
+      "attachments": {
+        "images/merge_sort.png": {
+          "image/png": 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pbWVuc2lvbj4KICAgICAgICAgPGV4aWY6UGl4ZWxYRGltZW5zaW9uPjE1MjQ8L2V4aWY6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\nWEUQpsjte43c4035mSMcEoC4KqEVMY3TBTxHWSibqlMD06ZOKxjxop4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1Za4ITnREYgrf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\n/VvbiJf2d/5eHlnvP9yWlnmHoP+dGXrffMvmvt+6QUm6KRv98m/e553XYHz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hf/f7P9T+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h0eVIFhjBPtUbgJuQCdegTbyGhl45kY9mPzr3fQ8fjjqLHdt7s31791rbk+3Yvt107ddY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\naQ8rP2lANllVhiWcKP1V4mm9HHqcVHe2vlHDJxo2O5OC4zD4GHMOtQAAQAB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VK1KyyAN2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\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NTfEZoCb+Qqa2ZzdznOMK0gG+AeC7dzXY88zz/qXnmuOnbOtHOnaftNmlhy5YzMi1eONY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\ngAABAp0E/h8NNSg//qJhWwAAAABJRU5ErkJggg==\n"
+        }
+      },
+      "cell_type": "markdown",
+      "id": "64fc2295-0840-4a40-842b-8329578348da",
+      "metadata": {},
+      "source": [
+        "-   Let’s work on a real problem next!\n",
+        "\n",
+        "## Mergesort\n",
+        "\n",
+        "-   Some lists don’t need to be sorted\n",
+        "\n",
+        "    -   Lists of size 1! This is our base case\n",
+        "\n",
+        "-   We can split lists in half until they contain 1 element, then merge\n",
+        "    all of the sub-lists\n",
+        "\n",
+        "-   Python’s sort function uses a hybrid of merge and insertion sort,\n",
+        "    both of which you’ve learned!\n",
+        "\n",
+        "![](attachment:images/merge_sort.png)\n",
+        "\n",
+        "## Big-O of Merge Sort\n",
+        "\n",
+        "First consider the non-recursive part of the code\n",
+        "\n",
+        "-   The “divide” step takes linear time, since slicing operations take\n",
+        "    roughly $n/2$ steps to make a left and right copy respectively.\n",
+        "\n",
+        "-   The merge operation also takes $n$ steps approximately\n",
+        "\n",
+        "-   All other operations are constants\n",
+        "\n",
+        "-   Together, the non-recursive part of this algorithm is $O(n)$\n",
+        "\n",
+        "Next consider the recursive calls\n",
+        "\n",
+        "-   Recall the big-O of recursion depends on the recursion depth and\n",
+        "    number of calls. $O(branches^{depth})$\n",
+        "\n",
+        "-   The depth in Merge Sort is the number of times you need to divide to\n",
+        "    get to a list of length 1.\n",
+        "\n",
+        "-   Mathematically, $2^{\\text{depth}} = n$, then\n",
+        "    $\\text{depth} = \\text{log}n$. So there are approximately log $n$\n",
+        "    levels\n",
+        "\n",
+        "## Big-O of Mergesort\n",
+        "\n",
+        "-   Since the $O(n)$ steps must be performed each recursion, the total\n",
+        "    run time is $O(n\\text{log}n)$. Our analysis only depended on the\n",
+        "    size of the list, so the best and worst case of mergesort is the\n",
+        "    same\n",
+        "\n",
+        "-   This is much faster than insertion sort!\n",
+        "\n",
+        "-   2 minutes: does it have less space complexity than insertion sort?\n",
+        "\n",
+        "# Recommended Problems and References\n",
+        "\n",
+        "## Recommended Problems\n",
+        "\n",
+        "-   Bhargava: Chapter 4 exercises\n",
+        "\n",
+        "    -   4.1 to 4.8\n",
+        "\n",
+        "-   Write a recursive function that produces the\n",
+        "    `RecursionError: maximum recursion depth exceeded` error.\n",
+        "\n",
+        "-   Write a iterative function to calculate the $n$th Fibonacci number.\n",
+        "    What is its time and space complexity?\n",
+        "\n",
+        "-   Write a recursive function to determine if a string is a palindrome.\n",
+        "    What is its time and space complexity?\n",
+        "\n",
+        "-   Write a recursive function to check if a given positive integer is a\n",
+        "    prime number. What is its time and space complexity?\n",
+        "\n",
+        "## Recommended Problems\n",
+        "\n",
+        "-   Suppose you have a plot of land and want to divide the land into\n",
+        "    even square plots, while keeping the plots as big as possible. How\n",
+        "    would you do this using D&C? See Bhargava pg. 52.\n",
+        "\n",
+        "-   Explain why the “merge” step in mergesort is $O(n)$\n",
+        "\n",
+        "-   Implement mergesort. You might find using helper functions useful.\n",
+        "\n",
+        "-   Write a recursive function to perform binary search on a sorted list\n",
+        "\n",
+        "## Bonus Readings\n",
+        "\n",
+        "-   You may be interested in learning more about quicksort in Bhargava\n",
+        "    chapter 4 or\n",
+        "    [here](https://www.teach.cs.toronto.edu/~csc148h/winter/notes/recursive-sorting/recursive_sorting.html).\n",
+        "    Quicksort is another recursive sorting method\n",
+        "\n",
+        "## References\n",
+        "\n",
+        "-   Bhargava, A. Y. (2016). *Grokking algorithms: An illustrated guide\n",
+        "    for programmers and other curious people.* Manning. Chapter 3 and 4.\n",
+        "\n",
+        "-   Cormen, T. H. (Ed.). (2009). *Introduction to algorithms* (3rd ed).\n",
+        "    MIT Press. Chapter 4."
+      ]
+    }
+  ],
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diff --git a/01_slides/4_recursive_ds.ipynb b/01_slides/4_recursive_ds.ipynb
new file mode 100644
index 0000000..bf2d95d
--- /dev/null
+++ b/01_slides/4_recursive_ds.ipynb
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yMiUyMHklM0QlMjIxODklMjIlMjBhcyUzRCUyMnRhcmdldFBvaW50JTIyJTIwJTJGJTNF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\nJSeneItAoUKFBIlSChYs6O1A2HtgEOD+1bdUrhD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+        },
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C0tigH/wBa6jkNVE5qEwF/Coyj99BRqggXWA+HIBxUItMQaQula2AX/JUhYNkBB1oDBOh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\nMkeul5/LhTJMjuC+qZP8A4zyrPgI2n3BAAAAAElFTkSuQmCC\n"
+        }
+      },
+      "cell_type": "markdown",
+      "id": "e606b619-c770-4437-9a7e-b5af87141aaf",
+      "metadata": {},
+      "source": [
+        "# Recursive Data Structures\n",
+        "\n",
+        "## Outline\n",
+        "\n",
+        "-   Trees\n",
+        "\n",
+        "-   Anatomy, tree traversal methods\n",
+        "\n",
+        "-   Binary Search Trees\n",
+        "\n",
+        "-   Graphs\n",
+        "\n",
+        "-   Nearest Neighbor Problem\n",
+        "\n",
+        "# Trees\n",
+        "\n",
+        "## Introduction to Trees\n",
+        "\n",
+        "-   Not all data has a natural linear order. Organization charts and\n",
+        "    file storage systems have a *hierarchical structure*, in which each\n",
+        "    entity is linked to multiple entities below it\n",
+        "\n",
+        "-   This type of data is represented using a *tree*. A tree is either\n",
+        "\n",
+        "    -   Empty\n",
+        "\n",
+        "    -   Has a *root value* connected to any number of other trees,\n",
+        "        called *subtrees*\n",
+        "\n",
+        "-   We draw the root at the top of the tree\n",
+        "\n",
+        "## Anatomy of a Tree\n",
+        "\n",
+        "-   The *size* of a tree is the number of values in the tree\n",
+        "\n",
+        "-   A *leaf* is a value with no subtrees. The leaves of this tree are\n",
+        "    labeled E, F, G, J, and I\n",
+        "\n",
+        "-   The *height* of a tree is the longest path from its root to its\n",
+        "    leaves. The height of this tree is 4\n",
+        "\n",
+        "![](attachment:images/tree.png)\n",
+        "\n",
+        "## Anatomy of a Tree\n",
+        "\n",
+        "-   The *children* of a value are all values directly connected\n",
+        "    underneath that value. The children of A are B, C, and D\n",
+        "\n",
+        "-   The *descendants* of a value are it’s children, the children of its\n",
+        "    children, etc. This can be defined recursively\n",
+        "\n",
+        "-   The *parent* of a value is the value immediately above and connected\n",
+        "    to it. The parent of H is C\n",
+        "\n",
+        "-   The *ancestors* of a value are its parent, the parent of its parent,\n",
+        "    etc.\n",
+        "\n",
+        "![](attachment:images/tree.png)\n",
+        "\n",
+        "## Tree Traversal Methods\n",
+        "\n",
+        "-   Linear data structures only have one logical way to traverse them.\n",
+        "    Trees can be traversed in different ways\n",
+        "\n",
+        "-   We’ll look at the following methods of tree traversal and their\n",
+        "    applications\n",
+        "\n",
+        "    -   *Depth First Search* (DFS): Inorder, Preorder, and Postorder\n",
+        "        traversal\n",
+        "\n",
+        "    -   *Breadth First Search* (BFS)\n",
+        "\n",
+        "-   Note there are other methods not covered\n",
+        "\n",
+        "## DFS: Inorder Traversal\n",
+        "\n",
+        "1.  Traverse the left subtree\n",
+        "\n",
+        "2.  Visit the root\n",
+        "\n",
+        "3.  Traverse the right subtree\n",
+        "\n",
+        "**Result: 4 2 5 1 6 3**\n",
+        "\n",
+        "![](attachment:images/tree_num.png)\n",
+        "\n",
+        "## DFS: Inorder Traversal Code\n",
+        "\n",
+        "Let’s look at the code to do this"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "4914a874",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "class Node:\n",
+        "  \"\"\"Tree class\n",
+        "  \"\"\"\n",
+        "  def __init__(self, key):\n",
+        "    self.left = None\n",
+        "    self.right = None\n",
+        "    self.val = key\n",
+        "    \n",
+        "def print_inorder(root):\n",
+        "  if root:\n",
+        "    print_inorder(root.left)\n",
+        "    print(root.val, end = \" \")\n",
+        "    print_inorder(root.right)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "abb121a0-9014-4411-8250-bb0fe5a46eee",
+      "metadata": {},
+      "source": [
+        "## DFS: Inorder Traversal Code"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "8e4b78cb",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "4 2 5 1 6 3 "
+          ]
+        }
+      ],
+      "source": [
+        "root = Node(1)\n",
+        "root.left = Node(2)\n",
+        "root.right = Node(3)\n",
+        "root.left.left = Node(4)\n",
+        "root.left.right = Node(5)\n",
+        "root.right.left = Node(6)\n",
+        "print_inorder(root)"
+      ]
+    },
+    {
+      "attachments": {
+        "images/insertion.png": {
+          "image/png": 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UGtUSoXTt7PtRq5CqFbYvsoBaBRXMQ75FkRHMxsKtXA5PpbswubE5o65U6S5w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\nZ3dhWQJ+xOykkrkpSxOj01BA9FoAABi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+        },
+        "images/tree_num.png": {
+          "image/png": 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yMiUyMHklM0QlMjIxODklMjIlMjBhcyUzRCUyMnRhcmdldFBvaW50JTIyJTIwJTJGJTNF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\nJSeneItAoUKFBIlSChYs6O1A2HtgEOD+1bdUrhD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+        },
+        "images/tree_unbal.png": {
+          "image/png": 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xJTIyJTIweSUzRCUyMjE4OSUyMiUyMGFzJTNEJTIydGFyZ2V0UG9pbnQlMjIlMjAlMkYl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\n"
+        }
+      },
+      "cell_type": "markdown",
+      "id": "91306dbf-a4f2-47f3-a88a-1c5da831baae",
+      "metadata": {},
+      "source": [
+        "In binary search trees (next section), inorder traversal gives the nodes\n",
+        "in a non-decreasing order.\n",
+        "\n",
+        "## DFS: Inorder Traversal Complexity\n",
+        "\n",
+        "Time complexity\n",
+        "\n",
+        "-   Each node is visited exactly once. The work done at each node is\n",
+        "    constant. $O(n)$\n",
+        "\n",
+        "Space complexity\n",
+        "\n",
+        "-   Dependent on the maximum depth of the recursion, which is the height\n",
+        "    of the tree. $O(h)$\n",
+        "\n",
+        "## DFS: Preorder Traversal\n",
+        "\n",
+        "1.  Visit the root\n",
+        "\n",
+        "2.  Traverse the left subtree\n",
+        "\n",
+        "3.  Traverse the right subtree\n",
+        "\n",
+        "**Result: 1 2 4 5 3 6**\n",
+        "\n",
+        "Preorder traversal is used to create a copy of the tree\n",
+        "\n",
+        "![](attachment:images/tree_num.png)\n",
+        "\n",
+        "## DFS: Postorder Traversal\n",
+        "\n",
+        "1.  Traverse the left subtree\n",
+        "\n",
+        "2.  Traverse the right subtree\n",
+        "\n",
+        "3.  Visit the root\n",
+        "\n",
+        "**Result: 4 5 2 6 3 1**\n",
+        "\n",
+        "Preorder traversal is used to delete subtrees. (why?)\n",
+        "\n",
+        "![](attachment:images/tree_num.png)\n",
+        "\n",
+        "## BFS\n",
+        "\n",
+        "BFS (or Level Order Traversal) traverses nodes present in the same level\n",
+        "before traversing the next level\n",
+        "\n",
+        "1.  For each node\n",
+        "\n",
+        "-   The node is visited\n",
+        "\n",
+        "-   The child nodes are enqueued in a FIFO queue\n",
+        "\n",
+        "1.  First node is dequeued\n",
+        "\n",
+        "2.  Child nodes are enqueued\n",
+        "\n",
+        "3.  Repeat until the queue is empty\n",
+        "\n",
+        "**Result: 1 2 3 4 5 6**\n",
+        "\n",
+        "![](attachment:images/tree_num.png)\n",
+        "\n",
+        "# Binary Search Trees\n",
+        "\n",
+        "## BST Definitions\n",
+        "\n",
+        "-   You can think of a BST as a sorted tree\n",
+        "\n",
+        "-   A *binary tree* is a tree in which every item has at most two\n",
+        "    subtrees\n",
+        "\n",
+        "    -   The tree used in illustrating DFS and BFS methods is a binary\n",
+        "        tree\n",
+        "\n",
+        "-   A binary tree is a *binary search tree property* if its value is\n",
+        "    greater than or equal to all items in the left subtree\n",
+        "\n",
+        "-   A binary tree is a *binary search tree* if every item in the tree\n",
+        "    satisfies the binary search tree property\n",
+        "\n",
+        "## BST Efficiency\n",
+        "\n",
+        "-   Consider the BST on the right. Verify that it is a BST.\n",
+        "\n",
+        "-   The worst-case run time is $O(h)$, $h$ being the height of the tree\n",
+        "\n",
+        "    -   So the tree on the right is $O(n)$\n",
+        "\n",
+        "-   A tree of height $h$ can have at most $2^h - 1$ nodes. So we need at\n",
+        "    least log$n$ height to store all of them.\n",
+        "\n",
+        "    -   So if the tree was balanced, then it would be $O(\\text{log}n)$\n",
+        "\n",
+        "![](attachment:images/tree_unbal.png)\n",
+        "\n",
+        "## BST Efficiency\n",
+        "\n",
+        "-   Convince yourself that for a balanced BST the search, insert, and\n",
+        "    delete Big-O is all $O(\\text{log}n)$\n",
+        "\n",
+        "-   Ensuring that a tree is balanced is important\n",
+        "\n",
+        "    -   Red-Black trees (not covered) are trees that balance themselves\n",
+        "\n",
+        "    -   You may also be interested in B-trees, which are used in\n",
+        "        databases\n",
+        "\n",
+        "## Live Coding\n",
+        "\n",
+        "Given a BST, insert a new node in this BST.\n",
+        "\n",
+        "![](attachment:images/insertion.png)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0fffca91",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "61fb5b0a",
+      "metadata": {},
+      "source": [
+        "\n",
+        "# Graphs\n",
+        "\n",
+        "## Introduction\n",
+        "\n",
+        "-   We looked at lists and trees, which represent linear and\n",
+        "    hierarchical relationships respectively\n",
+        "\n",
+        "    -   But many relationships are neither\n",
+        "\n",
+        "    -   Friend networks, internet connections, flight connections\n",
+        "\n",
+        "-   Graphs consist of two parts, *nodes* and *edges*\n",
+        "\n",
+        "    -   A node connected to another is a *neighbor*\n",
+        "\n",
+        "![](attachment:images/graph_anat.png)\n",
+        "\n",
+        "## Types of Graphs\n",
+        "\n",
+        "There are directed and undirected graphs to represent different\n",
+        "situations\n",
+        "\n",
+        "-   Friendships: undirected\n",
+        "\n",
+        "-   Twitter followers: directed\n",
+        "\n",
+        "-   Who owes who money: directed\n",
+        "\n",
+        "-   Note that trees are special cases of directed graphs\n",
+        "\n",
+        "Graphs can also be weighted, to differentiate strengths between nodes\n",
+        "\n",
+        "There are two questions we ask about graphs: Is there a path from node A\n",
+        "to B? What is the shortest path from node A to B? BFS answers both!\n",
+        "\n",
+        "![](attachment:images/graph_weight.png)\n",
+        "\n",
+        "## BFS of Graphs\n",
+        "\n",
+        "-   *Breadth First Search* (BFS) searches graph for a node that meets a\n",
+        "    set of criteria. It starts at the root of the graph and visits all\n",
+        "    nodes at the current depth level before moving on to the nodes at\n",
+        "    the next depth level\n",
+        "\n",
+        "    -   If there are multiple nodes meeting the criteria, then BFS will\n",
+        "        also find the nearest node!\n",
+        "\n",
+        "-   The issue is that graphs contain *cycles*, so we may visit the same\n",
+        "    node more than once\n",
+        "\n",
+        "    -   Let’s split edges into visited and not visited\n",
+        "\n",
+        "-   We use a list to keep track of visited nodes\n",
+        "\n",
+        "-   All the adjacent unvisited nodes of the current level are pushed\n",
+        "    into the queue and the nodes of the current level are marked visited\n",
+        "    and popped from the queue\n",
+        "\n",
+        "-   Is BFS a recursive or iterative graph search method?\n",
+        "\n",
+        "## BFS Example\n",
+        "\n",
+        "-   Let’s traverse a graph with BFS starting at node “1”\n",
+        "\n",
+        "-   Visited list and queue start as empty\n",
+        "\n",
+        "Visited: \\[ , , , , \\]\n",
+        "\n",
+        "Queue: \\[ , , , , \\]\n",
+        "\n",
+        "![](attachment:images/graph_bfs.png)\n",
+        "\n",
+        "## BFS Example\n",
+        "\n",
+        "-   We’re at node 1, so we push it onto the visited list and push it\n",
+        "    onto the queue\n",
+        "\n",
+        "Visited: \\[1, , , , \\]\n",
+        "\n",
+        "Queue: \\[1, , , , \\]\n",
+        "\n",
+        "![](attachment:images/graph_bfs.png)\n",
+        "\n",
+        "## BFS Example\n",
+        "\n",
+        "-   Now we visited 1, so it is dequeued.\n",
+        "\n",
+        "-   At the first level away from node 1, there is 3 and 6.\n",
+        "\n",
+        "-   We visit 3 and 6, but we have not visited any of it’s neighbors\n",
+        "    (other than 1), so 3 and 6 are enqueued.\n",
+        "\n",
+        "Visited: \\[1, 3, 6, , \\]\n",
+        "\n",
+        "Queue: \\[3, 6, , , \\]\n",
+        "\n",
+        "![](attachment:images/graph_bfs.png)\n",
+        "\n",
+        "## BFS Example\n",
+        "\n",
+        "-   Visit the neighbors of node 3, so we dequeue it\n",
+        "\n",
+        "-   But we need to enqueue 10, because we haven’t visited its neighbors\n",
+        "\n",
+        "Visited: \\[1, 3, 6, 10, \\]\n",
+        "\n",
+        "Queue: \\[6, 10, , \\]\n",
+        "\n",
+        "![](attachment:images/graph_bfs.png)\n",
+        "\n",
+        "## BFS Example\n",
+        "\n",
+        "-   Visit the neighbors of node 6, which is just 7, so we dequeue it\n",
+        "\n",
+        "-   But we need to enqueue 7\n",
+        "\n",
+        "Visited: \\[1, 3, 6, 10, 7\\]\n",
+        "\n",
+        "Queue: \\[10, 7, , \\]\n",
+        "\n",
+        "![](attachment:images/graph_bfs.png)\n",
+        "\n",
+        "## BFS Example\n",
+        "\n",
+        "-   Visit the neighbors of node 10, and dequeue 10\n",
+        "\n",
+        "-   But we already visited those nodes, so the visited list does not\n",
+        "    change\n",
+        "\n",
+        "Visited: \\[1, 3, 6, 10, 7\\]\n",
+        "\n",
+        "Queue: \\[7, , , , \\]\n",
+        "\n",
+        "![](attachment:images/graph_bfs.png)\n",
+        "\n",
+        "## BFS Example\n",
+        "\n",
+        "-   Visit neighbors of 7, which are also all visited\n",
+        "\n",
+        "-   The queue is empty, so the algorithm ends\n",
+        "\n",
+        "Visited: \\[1, 3, 6, 10, 7\\]\n",
+        "\n",
+        "Queue: \\[ , , , , \\]\n",
+        "\n",
+        "![](attachment:images/graph_bfs.png)\n",
+        "\n",
+        "## Time and Space Complexity of BFS\n",
+        "\n",
+        "-   Each edge and each node must be visited once, so the time complexity\n",
+        "    is $O(n + e)$\n",
+        "\n",
+        "-   Since we need to store each node of the graph by the end of the\n",
+        "    algorithm, the space complexity is $O(n)$\n",
+        "\n",
+        "## Implementing Graphs and BFS\n",
+        "\n",
+        "We can represent graphs using the *adjacency list* representation\n",
+        "\n",
+        "-   Other options include adjacency matrix or using a Python library"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "23428e44",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from collections import deque\n",
+        "\n",
+        "class Graph:\n",
+        "  def __init__(self):\n",
+        "    self.graph = {}\n",
+        "\n",
+        "  def add_edge(self, vertex, neighbors):\n",
+        "    self.graph[vertex] = neighbors"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9f393b7b-3af9-4a07-861c-9e14bc7bea47",
+      "metadata": {},
+      "source": [
+        "## Implementing Graphs and BFS"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "32ada213",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def bfs(graph, start):\n",
+        "  visited = set()\n",
+        "  queue = deque([start])\n",
+        "\n",
+        "  while queue:\n",
+        "    current_vertex = queue.popleft()\n",
+        "\n",
+        "    if current_vertex not in visited:\n",
+        "      # Process the current vertex\n",
+        "      print(current_vertex, end=' ')\n",
+        "      visited.add(current_vertex)\n",
+        "\n",
+        "      # Enqueue unvisited neighbors\n",
+        "      for neighbor in graph.graph.get(current_vertex, []):\n",
+        "        if neighbor not in visited:\n",
+        "          queue.append(neighbor)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3965d53d-8ca1-4372-8e0b-35a2a1486159",
+      "metadata": {},
+      "source": [
+        "## Implementing Graphs and BFS"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "3b8d278a",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "1 3 6 10 7 "
+          ]
+        }
+      ],
+      "source": [
+        "# Represent graph from above\n",
+        "ex_graph = Graph()\n",
+        "ex_graph.add_edge(1, [3, 6])\n",
+        "ex_graph.add_edge(3, [10, 6])\n",
+        "ex_graph.add_edge(6, [3, 7])\n",
+        "ex_graph.add_edge(10, [3, 7])\n",
+        "ex_graph.add_edge(7, [10, 6])\n",
+        "\n",
+        "# Perform BFS starting from vertex 1\n",
+        "bfs(ex_graph, 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "909bfd55-42c3-4c3c-8279-6489791e4f51",
+      "metadata": {},
+      "source": [
+        "## Recursive Graph Search: Preorder Traversal\n",
+        "\n",
+        "-   Using the same `Graph` class, let’s implement preorder traversal"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "d883118d",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def recursive_preorder_traversal(graph, start, visited=None):\n",
+        "  if visited is None:\n",
+        "    visited = set()\n",
+        "\n",
+        "  # Process the current vertex\n",
+        "  print(start, end=' ')\n",
+        "  visited.add(start)\n",
+        "\n",
+        "    # Recursive traversal of neighbors\n",
+        "  for neighbor in graph.graph.get(start, []):\n",
+        "    if neighbor not in visited:\n",
+        "      recursive_preorder_traversal(graph, neighbor, visited)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "60bf08d2-4747-41c3-b128-ff00d1bc771d",
+      "metadata": {},
+      "source": [
+        "## Recursive Graph Search: Preorder Traversal"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "c4b48ff8",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "1 3 6 10 7 "
+          ]
+        }
+      ],
+      "source": [
+        "bfs(ex_graph, 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c3b6c651-0046-4740-babf-41a312cf51bc",
+      "metadata": {},
+      "source": [
+        "# Nearest Neighour Problem\n",
+        "\n",
+        "## Nearest Neighbour Problem\n",
+        "\n",
+        "-   As you may have encountered already, machine learning and\n",
+        "    statistical methods often depend on finding the nearest neighbor to\n",
+        "    a data point\n",
+        "\n",
+        "    -   K-nearest neighbors regression, propensity score matching\n",
+        "\n",
+        "-   In a $k$ dimensional space, if we conduct a linear search for\n",
+        "    points, the running time will be $O(kn)$ for $n$ data points.\n",
+        "\n",
+        "-   Can we do better?\n",
+        "\n",
+        "## k-d Trees\n",
+        "\n",
+        "-   k-d trees is short for k dimensional tree (notation is a bit\n",
+        "    unfortunate, different K than KNN)\n",
+        "\n",
+        "    -   It is useful for multidimensional searches\n",
+        "\n",
+        "-   Let’s discuss the properties of k-d trees and why they work\n",
+        "\n",
+        "-   Binary tree where each node represents an axis-aligned\n",
+        "    hyperrectangle in the k-dimensional space\n",
+        "\n",
+        "    -   hyperrectangle: rectangle in higher dimensions\n",
+        "\n",
+        "-   Nodes in the left subtree have coordinates less than the splitting\n",
+        "    dimension of the current node, while nodes in the right subtree have\n",
+        "    coordinates greater than the splitting dimension.\n",
+        "\n",
+        "-   At each level of the tree, a specific dimension is chosen to split\n",
+        "    the data. The choice of dimension alternates as we traverse down the\n",
+        "    tree.\n",
+        "\n",
+        "-   Each leaf represents a single point in the k-dimensional space\n",
+        "\n",
+        "## k-d Trees Animation\n",
+        "\n",
+        "<https://commons.wikimedia.org/wiki/File:KDTree-animation.gif>\n",
+        "\n",
+        "## Applications and Issues\n",
+        "\n",
+        "-   Notice k-d trees can also find values within a certain range very\n",
+        "    quickly, not just a specific point\n",
+        "\n",
+        "-   GIS (geographic information systems) queries\n",
+        "\n",
+        "-   KNN algorithm\n",
+        "\n",
+        "-   Computer graphics, such as ray tracing to facilitate efficient space\n",
+        "    partitioning\n",
+        "\n",
+        "-   Issues occur in high-dimensional spaces and trees can become\n",
+        "    imbalanced\n",
+        "\n",
+        "# Recommended Problems and References\n",
+        "\n",
+        "## Recommended Readings\n",
+        "\n",
+        "-   Bhargava: Chapter 6\n",
+        "\n",
+        "-   Bhargava: Chapter 11, pages 203 to 206 about Trees\n",
+        "\n",
+        "## Recommended Problems\n",
+        "\n",
+        "-   Cormen: Chapter 10 exercises\n",
+        "\n",
+        "    -   10.3-1, 10.3-2, 10.3-3\n",
+        "\n",
+        "-   Bhargava: Chapter 6 exercises\n",
+        "\n",
+        "    -   6.1 to 6.5\n",
+        "\n",
+        "## Recommended Problems\n",
+        "\n",
+        "-   Implement preorder, postorder, and level order traversal. Determine\n",
+        "    the time and space complexity in each case\n",
+        "\n",
+        "-   Implement a function that find an element in a BST and deletes it.\n",
+        "    The descendants of the deleted node are given to the deleted node’s\n",
+        "    parent.\n",
+        "\n",
+        "-   Using the `graph` class from the slides, implement BST search such\n",
+        "    that it stops and tell you the distance the node is from the\n",
+        "    starting point.\n",
+        "\n",
+        "    -   For instance, if we searched for 7 in the graph given in the\n",
+        "        slides, it would return `\"Found! Distance 2\"`.\n",
+        "\n",
+        "    -   If we searched for 100 in the graph, it would return\n",
+        "        `\"Not found!\"`\n",
+        "\n",
+        "-   Implement postorder graph traversal using the `graph` class from the\n",
+        "    slides.\n",
+        "\n",
+        "-   Implement a function using recursion to find the sum heterogeneous\n",
+        "    nested lists such as \\[\\[1, \\[2\\]\\], \\[\\[\\[3\\]\\]\\], 4, \\[\\[5, 6\\],\n",
+        "    \\[\\[\\[7\\]\\]\\]\\]\\].\n",
+        "\n",
+        "## References\n",
+        "\n",
+        "-   Bhargava, A. Y. (2016). *Grokking algorithms: An illustrated guide\n",
+        "    for programmers and other curious people.* Manning. Chapter 6, 10,\n",
+        "    11.\n",
+        "\n",
+        "-   Cormen, T. H. (Ed.). (2009). *Introduction to algorithms* (3rd ed).\n",
+        "    MIT Press. Chapter 12 and 20.\n",
+        "\n",
+        "-   Horton, D., & Liu, D. (2023, November 19). *CSC148 Lecture Notes*.\n",
+        "    https://www.teach.cs.toronto.edu/~csc148h/winter/notes/"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": "3"
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
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diff --git a/02_assignments/assignment_1.ipynb b/02_assignments/assignment_1.ipynb
new file mode 100644
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+++ b/02_assignments/assignment_1.ipynb
@@ -0,0 +1,337 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Coding Problems\n",
+    "\n",
+    "## Objective\n",
+    "\n",
+    "This assignment aims to demonstrate how to study a data structures or algorithms question in depth to prepare for an industry coding interview. Leetcode is a popular coding practice site that many use to practice for technical interviews. Like behavioral interviews, it's important to practice and keep your skills sharp.\n",
+    "\n",
+    "## Group Size\n",
+    "\n",
+    "Please complete this individually.\n",
+    "\n",
+    "## Part 1:\n",
+    "\n",
+    "_*You will be assigned one of three problems based of your first name. Execute the code below, and that will tell you your assigned problem. Include the output as part of your submission (do not clear the output). The problems are based-off problems from Leetcode.*_\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print((hash('your first name') % 3) + 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details>\n",
+    "  <summary>Question 1</summary>\n",
+    "\n",
+    "  # Question One: Check Duplicates in Tree\n",
+    "\n",
+    "  Given the `root` of a binary tree, check whether it is contains a duplicate value. If a duplicate exists, return the duplicate value. If there are multiple duplicates, return the one with the closest distance to the root. If no duplicate exists, return -1.\n",
+    "\n",
+    "  ## Examples\n",
+    "\n",
+    "  ### Example 1\n",
+    "\n",
+    "  ![](images/q1_ex1.png \"Example 1\")\n",
+    "\n",
+    "  Input: `root = [1, 2, 2, 3, 5, 6, 7]` *What traversal method is this?*\n",
+    "\n",
+    "  Output: 2\n",
+    "\n",
+    "  ### Example 2\n",
+    "\n",
+    "  ![](images/q1_ex2.png \"Example 2\")\n",
+    "\n",
+    "  Input: `root = [1, 10, 2, 3, 10, 12, 12]`\n",
+    "\n",
+    "  Output: 10\n",
+    "\n",
+    "  ### Example 3\n",
+    "\n",
+    "  ![](images/q1_ex3.png \"Example 3\")\n",
+    "\n",
+    "  Input: `root = [10, 9, 8, 7]`\n",
+    "\n",
+    "  Output: -1\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "#### Starter Code for Question 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Definition for a binary tree node.\n",
+    "# class TreeNode(object):\n",
+    "#     def __init__(self, val = 0, left = None, right = None):\n",
+    "#         self.val = val\n",
+    "#         self.left = left\n",
+    "#         self.right = right\n",
+    "def is_symmetric(root: TreeNode) -> int:\n",
+    "  # TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details>\n",
+    "  <summary>Question 2</summary>\n",
+    "\n",
+    "  # Question Two: Path to Leaves\n",
+    "\n",
+    "  Given the `root` of a binary tree, return all root to leaf paths in any order.\n",
+    "\n",
+    "  ## Examples\n",
+    "\n",
+    "  ### Example 1\n",
+    "\n",
+    "  ![](images/q1_ex1.png \"Example 1\")\n",
+    "\n",
+    "  Input: `root = [1, 2, 2, 3, 5, 6, 7]` *What traversal method is this?*\n",
+    "\n",
+    "  Output: [[1, 2, 3], [1, 2, 5], [1, 2, 6], [1, 2, 7]]\n",
+    "\n",
+    "  ### Example 2\n",
+    "\n",
+    "  ![](images/q1_ex3.png \"Example 2\")\n",
+    "\n",
+    "  Input: `root = [10, 9, 8, 7]`\n",
+    "\n",
+    "  Output: [[10, 7], [10, 9, 8]]\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "#### Starter Code for Question 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Definition for a binary tree node.\n",
+    "# class TreeNode(object):\n",
+    "#     def __init__(self, val = 0, left = None, right = None):\n",
+    "#         self.val = val\n",
+    "#         self.left = left\n",
+    "#         self.right = right\n",
+    "def bt_path(root: TreeNode) -> List[List[int]]:\n",
+    "  # TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<details>\n",
+    "  <summary>Question 3</summary>\n",
+    "\n",
+    "  # Question Three: Missing Number in Range\n",
+    " \n",
+    "  You are given a list containing `n` integers in the range `[0, n]`. Return a list of numbers that are missing from the range `[0, n]` of the array. If there is no missing number, return -1. Note, all the integers in the list may not be unique.\n",
+    "  \n",
+    "  ## Examples\n",
+    "\n",
+    "  ### Example 1\n",
+    "\n",
+    "  Input: `lst = [0, 2]`\n",
+    "\n",
+    "  Output: [1]\n",
+    "\n",
+    "  ### Example 2\n",
+    "\n",
+    "  Input: `lst = [5, 0, 1]`\n",
+    "\n",
+    "  Output: [2, 3, 4]\n",
+    "\n",
+    "  ### Example 3\n",
+    "\n",
+    "  Input: `lst = [6, 8, 2, 3, 5, 7, 0, 1, 10]`\n",
+    "\n",
+    "  Output: [4, 9]\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "#### Starter Code for Question 3\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def missing_num(nums: List) -> int:\n",
+    "  # TODO"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Part 2:\n",
+    "\n",
+    "-   Paraphrase the problem in your own words\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "-   In the .md file containing your problem, there are examples that illustrate how the code should work. Create 2 new examples that demonstrate you understand the problem.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "-   Code the solution to your assigned problem in Python (code chunk). Try to find the best time and space complexity solution!\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "-   Explain why your solution works\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "-   Explain the problem’s and space complexity\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "-   Explain the thinking to an alternative solution (no coding required, but a classmate reading this should be able to code it up based off your text)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Submission Requirements\n",
+    "\n",
+    "Create and submit a public GitHub repository with the following:\n",
+    "\n",
+    "-   The PDF of the problem you have solved\n",
+    "\n",
+    "## Evaluation Criteria\n",
+    "\n",
+    "-   Problem is accurately stated in the student’s own words\n",
+    "\n",
+    "-   Two examples are correct and easily understandable\n",
+    "\n",
+    "-   Correctness, time, and space complexity of the coding solution\n",
+    "\n",
+    "-   Clarity in explaining why the solution works, its time and space complexity\n",
+    "\n",
+    "-   Clarity in the proposal to the alternative solution\n",
+    "\n",
+    "## Submission Deadline\n",
+    "TBD\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/02_assignments/assignment_2.ipynb b/02_assignments/assignment_2.ipynb
new file mode 100644
index 0000000..5cee768
--- /dev/null
+++ b/02_assignments/assignment_2.ipynb
@@ -0,0 +1,200 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Practice Interview\n",
+    "\n",
+    "## Objective\n",
+    "\n",
+    "_*The partner assignment aims to provide students with the opportunity to practice coding in an interview context. You will analyze your partner's Assignment 1. Moreover, code reviews are common practice in a software development team. This assignment should give you a taste of the code review process.*_\n",
+    "\n",
+    "## Group Size\n",
+    "\n",
+    "Each group should have 2 people. You will be assigned a partner\n",
+    "\n",
+    "## Part 1:\n",
+    "\n",
+    "You and your partner should send to each other your Assignment 1 submission."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Part 2:\n",
+    "\n",
+    "Create a Jupyter Notebook, create 6 of the following headings, and complete the following about the your partner's assignment 1:\n",
+    "\n",
+    "-   Paraphrase the problem in your own words.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "-   Create 1 new example that demonstrates you understand the problem. Trace/walkthrough 1 example that your partner made and explain it.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "-   Copy the solution your partner wrote. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "-   Explain why their solution works in your own words.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "-   Explain the problem’s time and space complexity in your own words.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "-   Critique your partner's solution, including explanation, if there is anything should be adjusted.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Part 3:\n",
+    "\n",
+    "Please write a 200 word reflection documenting your studying process from assignment 1, and your presentation and reviewing experience with your partner at the bottom of the Jupyter Notebook under a new heading \"Reflection.\" Again, export this Notebook as pdf.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reflection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your answer here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Submission Requirements \n",
+    "\n",
+    "In the same repository you submitted assignment 1, please ADD the following:\n",
+    "\n",
+    "-   The PDF of the Jupyter Notebook you created for Assignment 2.\n",
+    "\n",
+    "Please name your files appropriately!\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Evaluation Criteria\n",
+    "\n",
+    "We are looking for the similar points as Assignment 1\n",
+    "\n",
+    "-   Problem is accurately stated in the student’s own words\n",
+    "\n",
+    "-   New example is correct and easily understandable\n",
+    "\n",
+    "-   Correctness, time, and space complexity of the coding solution\n",
+    "\n",
+    "-   Clarity in explaining why the solution works, its time and space complexity\n",
+    "\n",
+    "-   Quality of critique of your partner's assignment, if necessary\n",
+    "\n",
+    "## Submission Deadline\n",
+    "\n",
+    "TBD\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/assignments/images/q1_ex1.png b/02_assignments/images/q1_ex1.png
similarity index 100%
rename from assignments/images/q1_ex1.png
rename to 02_assignments/images/q1_ex1.png
diff --git a/assignments/images/q1_ex2.png b/02_assignments/images/q1_ex2.png
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rename from assignments/images/q1_ex2.png
rename to 02_assignments/images/q1_ex2.png
diff --git a/assignments/images/q1_ex3.png b/02_assignments/images/q1_ex3.png
similarity index 100%
rename from assignments/images/q1_ex3.png
rename to 02_assignments/images/q1_ex3.png
diff --git a/03_homework/1_motivation_big_o_homework.ipynb b/03_homework/1_motivation_big_o_homework.ipynb
new file mode 100644
index 0000000..5d2bb01
--- /dev/null
+++ b/03_homework/1_motivation_big_o_homework.ipynb
@@ -0,0 +1,48 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "79078d67-1cad-401a-81b3-4d4c6b9548f3",
+      "metadata": {},
+      "source": [
+        "# 1: Motivation and Big-O\n",
+        "\n",
+        "-   Cormen: Chapter 1 exercises\n",
+        "\n",
+        "    -   1.2-1, 1.2-2, 1.2-3\n",
+        "\n",
+        "-   Bhargava: Chapter 1 exercises\n",
+        "\n",
+        "    -   1.3 to 1.5\n",
+        "\n",
+        "-   Additional (for the mathematically inclined)\n",
+        "\n",
+        "    -   In CS, log is usually base 2, but a strong distinction is not\n",
+        "        made because *logs of different bases only differ by a constant\n",
+        "        factor* and constants are dropped in Big-O. Show this is true\n",
+        "\n",
+        "    -   Show that exponents of different bases **do not** differ by a\n",
+        "        constant factor\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "4b64abc2",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/03_homework/2_ds_search_sort_homework.ipynb b/03_homework/2_ds_search_sort_homework.ipynb
new file mode 100644
index 0000000..03215bb
--- /dev/null
+++ b/03_homework/2_ds_search_sort_homework.ipynb
@@ -0,0 +1,116 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "c967b351",
+      "metadata": {},
+      "source": [
+        "# 2: Data Structures, Searching, and Sorting\n",
+        "\n",
+        "-   Bhargava: Chapter 5\n",
+        "\n",
+        "    -   5.1 to 5.4\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "46689452",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cdde6e73",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Give examples of 2 situations to use a queue and 2 situations to use\n",
+        "    a stack\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f167b945",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "69af89ae",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   In Python, code a `stack` class with `is_empty`, `push`, and `pop`\n",
+        "    methods using the end of a Python list as the top of the stack.\n",
+        "    Bonus: Compare the run time of using the start of the list versus\n",
+        "    the end of the list as the top of the stack using the `timeit`\n",
+        "    library!\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b7469ec3",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1ab22994",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   In Python, code a `binary_search` function.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b6220791",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a19b4c47",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   In Python, code a `hash_table` that can hash 4 values.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "e5fe66d6",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/03_homework/3_recursion_homework.ipynb b/03_homework/3_recursion_homework.ipynb
new file mode 100644
index 0000000..e0a7c03
--- /dev/null
+++ b/03_homework/3_recursion_homework.ipynb
@@ -0,0 +1,192 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "bc8494e5",
+      "metadata": {},
+      "source": [
+        "# 3: Recursion\n",
+        "\n",
+        "-   Bhargava: Chapter 4 exercises\n",
+        "    -   4.1 to 4.8\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b91dc2a6",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c6f4ba73",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Write a recursive function that produces the\n",
+        "    `RecursionError: maximum recursion depth exceeded` error.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "aad8ef5f",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "812428fe",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Write a iterative function to calculate the $n$th Fibonacci number.\n",
+        "    What is its time and space complexity?\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "8707a768",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4f69b976",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Write a recursive function to determine if a string is a palindrome.\n",
+        "    What is its time and space complexity?\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1e77b6d6",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a25c6ecc",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Write a recursive function to check if a given positive integer is a\n",
+        "    prime number. What is its time and space complexity?\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "09f85e69",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "abe26857",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Suppose you have a plot of land and want to divide the land into\n",
+        "    even square plots, while keeping the plots as big as possible. How\n",
+        "    would you do this using D&C? See Bhargava pg. 52.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "ffb7e377",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7b8b36ed",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Explain why the “merge” step in mergesort is $O(n)$\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b3176290",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "2ded9965",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Implement mergesort. You might find using helper functions useful.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "f9447db8",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "44d62627",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Write a recursive function to perform binary search on a sorted list\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b6a972f3",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/03_homework/4_recursive_ds_homework.ipynb b/03_homework/4_recursive_ds_homework.ipynb
new file mode 100644
index 0000000..7362380
--- /dev/null
+++ b/03_homework/4_recursive_ds_homework.ipynb
@@ -0,0 +1,144 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "df8050ef",
+      "metadata": {},
+      "source": [
+        "# 4: Recursive Data Structures\n",
+        "\n",
+        "-   Cormen: Chapter 10 exercises\n",
+        "    -   10.3-1, 10.3-2, 10.3-3\n",
+        "\n",
+        "-   Bhargava: Chapter 6 exercises\n",
+        "    -   6.1 to 6.5\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "305754c9",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "572471d3",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Implement preorder, postorder, and level order traversal. Determine\n",
+        "    the time and space complexity in each case\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "677f9d8f",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0bf7e104",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Implement a function that find an element in a BST and deletes it.\n",
+        "    The descendants of the deleted node are given to the deleted node’s\n",
+        "    parent. (Hard)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "517880a2",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3f935680",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Using the `graph` class from the slides, implement BST search such\n",
+        "    that it stops and tell you the distance the node is from the\n",
+        "    starting point.\n",
+        "    -   For instance, if we searched for 7 in the graph given in the\n",
+        "        slides, it would return `\"Found! Distance 2\"`.\n",
+        "    -   If we searched for 100 in the graph, it would return\n",
+        "        `\"Not found!\"`\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "1a51097c",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "24183ade",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Implement postorder graph traversal using the `graph` class from the\n",
+        "    slides.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "d6d4674c",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "da38771b",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Implement a function using recursion to find the sum heterogeneous\n",
+        "    nested lists such as \\[\\[1, \\[2\\]\\], \\[\\[\\[3\\]\\]\\], 4, \\[\\[5, 6\\],\n",
+        "    \\[\\[\\[7\\]\\]\\]\\]\\]."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "dd765491",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/04_instructors/README.md b/04_instructors/README.md
new file mode 100644
index 0000000..49ca7e3
--- /dev/null
+++ b/04_instructors/README.md
@@ -0,0 +1,55 @@
+# Technical Facilitator Playbook
+
+## How do you interact with the repo?
+The Technical Facilitator will teach using the content provided in the `/01_slides` directory. You are allowed to live code with them during your lecture. Just make sure you upload the live_coding files to a new directory called `/live_coding` under `/01_slides` to this repository using a new branch and open up a pull request for it to be merged.
+
+## How does the module flow?
+The module is organized into 4 main directories:
+1. Slides
+2. Assignments
+3. Homework
+
+The `/01_slides` directory would be the first directory anyone would see, and contains the lecture slides, learning outcomes, and anything related to the learning outcomes such as live_coding files.
+
+The `/02_assignments` directory is the second directory containing the assignments that would be marked as a `pass` or `fail`. Compared to `/04_homework`, the assignments are slightly more difficult and help encapsulate the learnings for the week. All assignments are mandatory to complete and deliver.
+
+The `/04-homework` directory contains all the homework, a learner can do to demonstrate mastery of the learning contents. Unlike the `/02_assignments` directory, all homework is optional but is highly encouraged to attempt.
+
+### Week 1
+
+| Lesson | Topic                                                       | Resources  |
+|--------|-------------------------------------------------------------|------------|
+| 1      | Motivation and Big-O Notation                               | [Slides](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/lessons/1_motivation-big-o.pdf) |
+| 2      | Data Structures, Sorting, and Searching                     | [Slides](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/lessons/2_ds-search-sort.pdf) |
+| 3      | Recursion              				                             | [Slides](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/lessons/3_recursion.pdf) |
+
+### Week 2
+
+| Lesson | Topic                                                       | Resources  |
+|--------|-------------------------------------------------------------|------------|
+| 4      | Recursion              				                             | [Slides](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/lessons/3_recursion.pdf) |
+| 5      | Recursive Data Structures                                   | [Slides](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/lessons/4_recursive-ds.pdf) |
+
+## How do you assign assignments?
+Assignments are given and assigned at the start of each week at the end of the first lecture. The Technical Facilitator will announce to the learners what the assignment is about, and how everything they will learn within the week will equip them to work on the assignment.
+
+## How an assignment is expected to be completed and delivered?
+Learners are expected to complete the assignment before the first lecture of the following week. They will deliver the assignment by opening up a pull request on their copied repo. The learner will also add a Learning Support Staff as a reviewer indicating they delivered a completed assignment, and it is ready to be graded as a `pass` or `fail`.
+
+## What are the criteria for `pass` or `fail`?
+The criteria for a `pass` or `fail` is if all parts of the program are working, and nothing in the code is broken. For some assignments, a rubric will be given outlining the criteria needed to pass.
+
+## How to mark?
+If the learner's solution works, then they `pass`! You or the Learning Support Staff would only need to focus on providing them constructive feedback on how they can improve their code. If the solution does not work, then they `fail`, and you would provide any constructive feedback on their existing code, and encourage them to get their solution working.
+
+## How will feedback be given?
+Feedback will be given through the pull request a learner had made, allowing them to make revisions if needed. To maximize learning, feedback must be constructive, and specific.
+
+<hr>
+
+## Definitions
+**Lecture**: A Lecture is a synchronous period, lasting up to 3 hours, where the Technical Facilitator will facilitate and deliver the contents and learning outcomes online through Zoom. Learners are encouraged to participate and ask questions as they learn. Breaks are given if the lecture goes past an hour, with a 10-minute break given for every hour still in a lecture.
+
+**Work Period**: A Work Period is an asynchronous period, lasting up to 3 hours. The learners will work on their assignments and/or homework during this block of time. A Learning Support Staff will be present online through Zoom to assist the learners and answer any questions they may have. As work periods are asynchronous and flexible, learners can choose to work on their own time. However, it is encouraged to work during the block of time when a Learning Support Staff is present.
+
+**Assignments**: An Assignment is a work assigned as part of the certification. They are slightly more difficult, providing an opportunity for learners to integrate and synthesize what they have learned throughout the week to meet the set learning outcomes.
diff --git a/05_additional_resources/homework/5_optimization_homework.ipynb b/05_additional_resources/homework/5_optimization_homework.ipynb
new file mode 100644
index 0000000..78d3795
--- /dev/null
+++ b/05_additional_resources/homework/5_optimization_homework.ipynb
@@ -0,0 +1,97 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "7b7386ac",
+      "metadata": {},
+      "source": [
+        "# 5: Optimization\n",
+        "\n",
+        "-   Bhargava: Chapter 9 exercises\n",
+        "    -   9.1, 9.2\n",
+        "    -   Read the knapsack problem FAQs on page 171\n",
+        "    -   Follow the example about longest common substring on page 178\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "992abc26",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5349aa98",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Write the code to brute force the diet problem. Compare the run\n",
+        "    times using the `timeit` library.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "402edd57",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "12f36004",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Modify the code from the slide such that there is an upper bound for\n",
+        "    calories and vitamins.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "93376267",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "12fe4558",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Page 17 of Bhargava covered the traveling sales person problem. Is\n",
+        "    it possible to improve the proposed solution using any method we\n",
+        "    learned today?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a88cb091",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/05_additional_resources/homework/6_slow-code_homework.ipynb b/05_additional_resources/homework/6_slow-code_homework.ipynb
new file mode 100644
index 0000000..20e29de
--- /dev/null
+++ b/05_additional_resources/homework/6_slow-code_homework.ipynb
@@ -0,0 +1,91 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "55954002",
+      "metadata": {},
+      "source": [
+        "# 6: Slow Code\n",
+        "\n",
+        "-   Bhargava: Chapter 8 exercises\n",
+        "    -   8.1 - 8.8\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "8643da00",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4a726575",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   Vectorize the second code chunk in the Parallelization section of the lesson 6 slides\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "286b4a12",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6e608f44",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   [Find the longest palindrome from a string](https://leetcode.com/problems/longest-palindrome/) Hint: use a greedy algorithm\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "48666b82",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "9a3f618a",
+      "metadata": {},
+      "source": [
+        "\n",
+        "-   [Computing Pascal’s triangle](https://leetcode.com/problems/pascals-triangle/) Hint: use dynamic programming"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "bb5fb8be",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Your code here"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/05_additional_resources/lessons/5_optimization.ipynb b/05_additional_resources/lessons/5_optimization.ipynb
new file mode 100644
index 0000000..629d63f
--- /dev/null
+++ b/05_additional_resources/lessons/5_optimization.ipynb
@@ -0,0 +1,436 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "c69aac2a",
+      "metadata": {},
+      "source": [
+        "# Optimization\n",
+        "\n",
+        "## Outline\n",
+        "\n",
+        "-   Setting up an Optimization Problem\n",
+        "\n",
+        "-   Dynamic Programming\n",
+        "\n",
+        "# Setting up an Optimization Problem\n",
+        "\n",
+        "## Types of Optimization Problems\n",
+        "\n",
+        "-   *Optimization* refers to maximizing or minimizing a function with respect to its inputs\n",
+        "\n",
+        "-   Continuous optimization is when all the variables in the problem are continuous\n",
+        "\n",
+        "-   Discrete optimization occurs when some or all of the variables in the problem are discrete\n",
+        "\n",
+        "    -   Continuous: how many hours should workers in a factory work to maximize profits?\n",
+        "\n",
+        "    -   Discrete: how do I allocate TAs to teach within a department?\n",
+        "\n",
+        "## Autocorrect Example\n",
+        "\n",
+        "-   Autocorrect in an optimization algorithm. It has two parts\n",
+        "\n",
+        "    -   We need a list of known words and their use frequency\n",
+        "\n",
+        "    -   Classify errors are either: add a letter, remove a letter, substitute a letter, or switched two adjacent letters\n",
+        "\n",
+        "-   We quantify the error distance as the of errors in a string.\n",
+        "\n",
+        "    -   \"ovon\" -\\> \"oven\" is error distance 1\n",
+        "\n",
+        "    -   \"ovvvn\" -\\> \"ovven\" -\\> \"oven\" is error distance 2\n",
+        "\n",
+        "## Autocorrect Example\n",
+        "\n",
+        "1.  Check whether a word is in the dictionary\n",
+        "\n",
+        "2.  If the word is not in the dictionary, generate words that are error distance 1 or 2 from the given word\n",
+        "\n",
+        "3.  Rank the most likely correction given the error distance and use frequency\n",
+        "\n",
+        "-   \"thene\" could be \"then\" or \"the,\" but but \"the\" is more common\n",
+        "\n",
+        "## Autocorrect Example\n",
+        "\n",
+        "What are the steps to model the problem?\n",
+        "\n",
+        "-   We have the specification of possible inputs\n",
+        "\n",
+        "    -   Text, discrete\n",
+        "\n",
+        "-   The *objective function* is the function you are trying to maximize more minimize\n",
+        "\n",
+        "    -   Function with 2 variables: error distance and frequency\n",
+        "\n",
+        "-   Are we maximizing or minimizing the objective function\n",
+        "\n",
+        "    -   Minimize error distance and maximize frequency\n",
+        "\n",
+        "-   Identify the *constraints* in the problem\n",
+        "\n",
+        "    -   Only looking for words in the dictionary, only looking for words with error distance 1 or 2\n",
+        "\n",
+        "## Shortest Path in a Graph Example\n",
+        "\n",
+        "-   Finding the shortest path between two nodes on a graph is a discrete optimization problem\n",
+        "\n",
+        "-   The range of inputs are all possible paths from A to B\n",
+        "\n",
+        "-   The objective function is the length of the path\n",
+        "\n",
+        "-   We are minimizing the objective function\n",
+        "\n",
+        "-   And there are no constraints\n",
+        "\n",
+        "## Brute Force\n",
+        "\n",
+        "Consider the following problems and proposed solutions\n",
+        "\n",
+        "-   You want to consume all necessary nutrients and calories at the lowest cost. So, you find all valid combinations of foods and find their cost.\n",
+        "\n",
+        "    -   If there are 10 foods, and 15 nutritional categories, then there are $2^{10 \\times 15} = 1.42 \\times 10^{45}$ combinations to evaluate\n",
+        "\n",
+        "    -   We will fix this with *linear programming*\n",
+        "\n",
+        "-   You are robbing a store but the escape vent can only carry 4 kg of goods. To steal the maximum money's worth of goods, you calculate every set of goods and find the one giving the most value\n",
+        "\n",
+        "    -   If there are 3 goods in the store, then there are 8 combinations. But with 4 goods, there are 16 combinations. This solution is $O(2^n)$ time.\n",
+        "\n",
+        "    -   We will fix this with *dynamic programming*\n",
+        "\n",
+        "# Linear Programming\n",
+        "\n",
+        "## Linear Programming\n",
+        "\n",
+        "-   Linear programming (LP) takes advantage of a program being linear. (what does that mean?)\n",
+        "\n",
+        "-   If we're considering a food that already fills one nutrition category, we can eliminate all other combinations that use the food\n",
+        "\n",
+        "    -   Sounds obvious, but brute forcing doesn't consider this!\n",
+        "\n",
+        "-   By this process of elimination, we make the problem much faster to solve.\n",
+        "\n",
+        "## Implementing LP in Python\n",
+        "\n",
+        "Let's consider a very simple diet problem where the goal is to minimize the cost. There are 3 foods: apples (\\$3), bananas (\\$1), and oranges (\\$3). We want to meet 3 constraints: of vitamin A, a number of vitamin B, and a number of calories.\n",
+        "\n",
+        "-   Assume there is no upper limit on calories or vitamins\n",
+        "\n",
+        "-   The PuLP library is a popular linear programming library to do this in Python\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "76bd880b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from pulp import LpProblem, LpMinimize, LpVariable, lpSum"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "c2b917d4",
+      "metadata": {},
+      "source": [
+        "## Implementing LP in Python\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "6760b8ad",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#| output: false\n",
+        "diet_problem = LpProblem(\"Diet_Problem\", LpMinimize)\n",
+        "\n",
+        "# Define output variables\n",
+        "x1 = LpVariable(\"Apples\", lowBound=0)\n",
+        "x2 = LpVariable(\"Bananas\", lowBound=0)\n",
+        "x3 = LpVariable(\"Oranges\", lowBound=0)\n",
+        "\n",
+        "# Define objective function (minimize cost)\n",
+        "diet_problem += 3 * x1 + x2 + 3 * x3, \"Total_Cost\"\n",
+        "\n",
+        "# Define nutritional constraints\n",
+        "diet_problem += 50 * x1 + 120 * x2 + 60 * x3 >= 2000, \"Calories\"\n",
+        "diet_problem += 2 * x1 + 3 * x2 + 5 * x3>= 40, \"Vitamin A\"\n",
+        "diet_problem += 12 * x1 + x2 + 2 * x3>= 50, \"Vitamin B\"\n",
+        "\n",
+        "diet_problem.solve()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "09f0b203",
+      "metadata": {},
+      "source": [
+        "## Implementing LP in Python\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b5cd1297",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "print(\"Optimal Diet:\")\n",
+        "print(f\"Apples: {round(x1.value(), 2)} units\")\n",
+        "print(f\"Bananas: {round(x2.value(), 2)} units\")\n",
+        "print(f\"Oranges: {round(x3.value(), 2)} units\")\n",
+        "print(f\"Total Cost: {round(diet_problem.objective.value(), 2)}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4686b0d5",
+      "metadata": {},
+      "source": [
+        "# Dynamic Programming\n",
+        "\n",
+        "## Problem\n",
+        "\n",
+        "The escape vent can carry only 4 kg of goods. The items are:\n",
+        "\n",
+        "-   Stereo: \\$3000, 4 kg\n",
+        "-   Laptop: \\$2000, 3 kg\n",
+        "-   Guitar: \\$1500, 1 kg\n",
+        "\n",
+        "We've established the brute force is not a valid general solution (although feasible in this case)\n",
+        "\n",
+        "-   The idea behind dynamic programming is that we'll solve subproblems that will lead to a solution to the big problem. We can pack items starting by considering smaller, sub backpacks\n",
+        "\n",
+        "## Guitar Row\n",
+        "\n",
+        "-   Each dynamic programming problem starts with a grid\n",
+        "-   Each cell contains a list of items that can fit at that point\n",
+        "-   For cell Guitar 1, a guitar will fit there. It will also fit in cell Guitar 2, 3, 4\n",
+        "-   Sounds redundant, but let's keep going\n",
+        "\n",
+        "![](images/dynamic.png)\n",
+        "\n",
+        "## Stereo Row\n",
+        "\n",
+        "-   In the second row, we can steal the stereo or the guitar.\n",
+        "-   At 1 kg, you can only steal the guitar, same as for every other cell until Stereo 4, at which point you can steal the stereo and only the stereo.\n",
+        "\n",
+        "![](images/dynamic.png)\n",
+        "\n",
+        "## Laptop Row\n",
+        "\n",
+        "-   Now we can steal all 3 items\n",
+        "-   In the first two columns, we still can only steal the guitar. But in Laptop 3, we can steal the laptop\n",
+        "-   Laptop 4 is the interesting step. We could steal only the stereo, or the laptop and something else for 1 kg. What is that 1 kg item?\n",
+        "-   According to the above row, the max value for 1 kg is the guitar!\n",
+        "\n",
+        "![](images/dynamic.png)\n",
+        "\n",
+        "## Solution\n",
+        "\n",
+        "-   If we stole the guitar and laptop, the total value is 3500, which is greater than just stealing the stereo\n",
+        "-   Thus, we should steal guitar and laptop\n",
+        "\n",
+        "![](images/dynamic.png)\n",
+        "\n",
+        "## Formula for each cell\n",
+        "\n",
+        "-   We skipped some very trivial steps in calculating cells aside from the last one\n",
+        "-   Here's the explicit formula to calculate each cell's value\n",
+        "\n",
+        "Let $i$ be the row and $j$ be the column.\n",
+        "\n",
+        "$$\n",
+        "\\text{cell}[i][j] = \\max\n",
+        "    \\begin{cases}\n",
+        "        \\text{the previous max at cell}[i-1][j]\\\\\n",
+        "        \\text{value of current item + value of remaining space}\n",
+        "    \\end{cases}\n",
+        "$$\n",
+        "\n",
+        "The value of remaining space is cell\\[i-1\\]\\[j-item's weight\\]\n",
+        "\n",
+        "## Python Implementation\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "06dd385b",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def initialize_table(rows, cols):\n",
+        "  return [[0] * cols for _ in range(rows)]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3fe47df4",
+      "metadata": {},
+      "source": [
+        "## Python Implementation\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "c930c579",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def knapsack_dynamic_programming(values, weights, capacity):\n",
+        "  n = len(values)\n",
+        "  dp = initialize_table(n + 1, capacity + 1)\n",
+        "\n",
+        "  # Fill the table using dynamic programming\n",
+        "  for i in range(1, n + 1):\n",
+        "    for w in range(capacity + 1):\n",
+        "      # Include the current item if it fits in the knapsack\n",
+        "      if weights[i - 1] <= w:\n",
+        "        dp[i][w] = max(dp[i - 1][w], \\\n",
+        "        values[i - 1] + dp[i - 1][w - weights[i - 1]])\n",
+        "      else:\n",
+        "        dp[i][w] = dp[i - 1][w]\n",
+        "\n",
+        "  selected_items = traceback(dp, values, weights, capacity)\n",
+        "\n",
+        "  return dp[n][capacity], selected_items"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "8e43fb8a",
+      "metadata": {},
+      "source": [
+        "## Python Implementation\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b9969e85",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def traceback(dp, weights, capacity):\n",
+        "  selected_items = []\n",
+        "  i, w = len(dp) - 1, capacity\n",
+        "\n",
+        "  while i > 0 and w > 0:\n",
+        "    if dp[i][w] != dp[i - 1][w]:\n",
+        "      selected_items.append(i - 1)\n",
+        "      w -= weights[i - 1]\n",
+        "      i -= 1\n",
+        "\n",
+        "  selected_items.reverse()\n",
+        "  return selected_items"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f76524a7",
+      "metadata": {},
+      "source": [
+        "## Python Implementation\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "b7312b2e",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "values = [3000, 2000, 1500]\n",
+        "weights = [4, 3, 1]\n",
+        "capacity = 4\n",
+        "\n",
+        "max_value, selected_items = \\\n",
+        "knapsack_dynamic_programming(values, weights, capacity)\n",
+        "\n",
+        "print(\"Maximum value:\", max_value)\n",
+        "print(\"Selected items:\", selected_items)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f7630cbc",
+      "metadata": {},
+      "source": [
+        "## Live Coding\n",
+        "\n",
+        "Let a substring be *upper-lower* if for every letter of the alphabet that the string contains, it appears both in uppercase and lowercase. For example, `aaA` is upper-lower because it has both \"A\" and \"a.\" `aAbb` is not upper-lower because it lacks an upper case \"B.\"\n",
+        "\n",
+        "Given string, return the longest substring that is *upper-lower.*\n",
+        "\n",
+        "### Example\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "8ada5a46",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#| eval: false\n",
+        "# INPUT\n",
+        "string = \"AqeQEfa\"\n",
+        "# OUTPUT\n",
+        "\"qeQE\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a8afc9a9",
+      "metadata": {},
+      "source": [
+        "# Recommended Problems and References\n",
+        "\n",
+        "## Recommended Problems and Readings\n",
+        "\n",
+        "-   Cormen (highly optional):\n",
+        "\n",
+        "-   Chapter 14, more advanced dynamic programming\n",
+        "\n",
+        "-   Chapter 29, more advanced linear programming\n",
+        "\n",
+        "-   Bhargava: Chapter 9 exercises\n",
+        "\n",
+        "    -   9.1, 9.2\n",
+        "\n",
+        "    -   Read the knapsack problem FAQs on page 171\n",
+        "\n",
+        "    -   Follow the example about longest common substring on page 178\n",
+        "\n",
+        "## Recommended Problems\n",
+        "\n",
+        "-   Write the code to brute force the diet problem. Compare the run times using the `timeit` library.\n",
+        "\n",
+        "-   Modify the code from the slide such that there is an upper bound for calories and vitamins.\n",
+        "\n",
+        "-   Page 17 of Bhargava covered the travelling sales person problem. Is it possible to improve the proposed solution using any method we learned today?\n",
+        "\n",
+        "## References\n",
+        "\n",
+        "-   Bhargava, A. Y. (2016). *Grokking algorithms: An illustrated guide for programmers and other curious people.* Manning. Chapter 1.\n",
+        "\n",
+        "-   Cormen, T. H. (Ed.). (2009). *Introduction to algorithms* (3rd ed). MIT Press. Chapter 1 and 3."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/05_additional_resources/lessons/6_slow_code.ipynb b/05_additional_resources/lessons/6_slow_code.ipynb
new file mode 100644
index 0000000..7392192
--- /dev/null
+++ b/05_additional_resources/lessons/6_slow_code.ipynb
@@ -0,0 +1,462 @@
+{
+  "cells": [
+    {
+      "attachments": {
+        "./images/memo.png": {
+          "image/png": 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TWTbOgonpwklL2A5uTBeurwpR187cqlPlLUmRbulJ/j5uAqYrArB+Da2yw4UN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JarMchy9LFd8IaxD/1xxrXFSf4jvK2we5ye2GzWa8tDtxypulWLzzMAZqS9S/n3ny6/mB7k7yFk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\n1N+EXC6n3Ok5cOBA/W/RzUE3T1vQ2L1DD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+        }
+      },
+      "cell_type": "markdown",
+      "id": "8be3d6b3-42a8-45cc-b9f7-4d8ee3288731",
+      "metadata": {},
+      "source": [
+        "# Why is my code slow?\n",
+        "\n",
+        "## Outline\n",
+        "\n",
+        "-   Caching, Memoization, and Vectorization\n",
+        "\n",
+        "-   Parallel Computing\n",
+        "\n",
+        "-   Greedy and Exhaustive Algorithms\n",
+        "\n",
+        "-   Faster Implementations versus Faster Algorithms\n",
+        "\n",
+        "# Caching, Memoization, and Vectorization\n",
+        "\n",
+        "## Caching\n",
+        "\n",
+        "-   *Caching* refers to storing things for later use\n",
+        "\n",
+        "    -   Your browser probably does by temporarily downloading page\n",
+        "        details on your local disk\n",
+        "\n",
+        "    -   Faster, reduces server load\n",
+        "\n",
+        "    -   Other examples include 3D rendering and saving common database\n",
+        "        queries\n",
+        "\n",
+        "-   However, caching usually takes space in exchange for faster run\n",
+        "    times\n",
+        "\n",
+        "-   The *space-time* trade off is a case where an algorithm trades\n",
+        "    increased space usage for faster runtimes\n",
+        "\n",
+        "## Memoization\n",
+        "\n",
+        "-   *Memoization* refers to storing results of function calls to use for\n",
+        "    later\n",
+        "\n",
+        "    -   Specific method of caching\n",
+        "\n",
+        "-   This is useful for methods with a lot of repeated computations\n",
+        "\n",
+        "-   For instance, in our recursive Fibonacci number function.\n",
+        "\n",
+        "-   `fib(12)` is called by `fib(13)`, `fib(14)` etc.\n",
+        "\n",
+        "    -   And `fib(3)` is called many many times\n",
+        "\n",
+        "-   $F(5) = F(4) + F(3) = F(3) + F(2) + F(2) + F(1)$ Which calculates\n",
+        "    repeated subproblems\n",
+        "\n",
+        "## How Memoization Works\n",
+        "\n",
+        "-   Since we store the results, each function call is only made once,\n",
+        "    making the time complexity $O(n)$, much better than $O(2^n)$ [1]\n",
+        "\n",
+        "-   Memoization can also avoid the maximum recursion depth error because\n",
+        "    the call stack is smaller\n",
+        "\n",
+        "![](attachment:./images/memo.png)\n",
+        "\n",
+        "## Memoization Python\n",
+        "\n",
+        "[1] From Bhargava chapter 8"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "41c39807",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "cache = {0: 0, 1: 1}\n",
+        "\n",
+        "def fib(n):\n",
+        "  if n in cache:\n",
+        "    return cache[n]\n",
+        "  else:\n",
+        "    cache[n] = fib(n - 1) + fib(n - 2)\n",
+        "    return cache[n]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6bc77d06-4eb3-4a2b-a782-a24d86bfb804",
+      "metadata": {},
+      "source": [
+        "For the base cases, we replace calling `fib(0)` and `fib(1)` by getting\n",
+        "the values from the dictionary\n",
+        "\n",
+        "## Memoization Python\n",
+        "\n",
+        "-   We can use the `functools` library, which is included in the\n",
+        "    standard library (no pip install needed!)\n",
+        "\n",
+        "    -   `functools` does memoization for you!\n",
+        "\n",
+        "-   We can use the `@cache` decorator, but the cached dictionary can\n",
+        "    grow to massive sizes\n",
+        "\n",
+        "-   Instead, `@lru_cache(maxsize = n)` uses the LRU (least recently\n",
+        "    used) `n` computations\n",
+        "\n",
+        "-   Alternatively, we can use `joblib` to store the memoized results in\n",
+        "    a file\n",
+        "\n",
+        "## Memoization Python"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "0574f8be",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "from functools import lru_cache\n",
+        "\n",
+        "@lru_cache(maxsize=10)\n",
+        "def fib_rec(n):\n",
+        "  if n == 0 or n == 1:\n",
+        "    return n\n",
+        "  else:\n",
+        "    return fib_rec(n-1) + fib_rec(n-2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "e9bacef3-929c-4e5d-a3c1-cdbdb4fc23c0",
+      "metadata": {},
+      "source": [
+        "## Vectorized Operations\n",
+        "\n",
+        "-   *Vectorization* is a technique of implementing array operations\n",
+        "    without for loops\n",
+        "\n",
+        "-   We use functions defined by various modules that are highly\n",
+        "    optimized for the specific problem\n",
+        "\n",
+        "-   NumPy provides a lot of functions that vectorized and are faster\n",
+        "    than for loops\n",
+        "\n",
+        "    -   Array add/subtract/multiply/divide by scalar\n",
+        "\n",
+        "    -   Sum of array\n",
+        "\n",
+        "    -   Max/min of array\n",
+        "\n",
+        "-   Keep this in mind for some ML processes that are iterative, such as\n",
+        "    gradient descent\n",
+        "\n",
+        "## Why Vectorized Operations Work\n",
+        "\n",
+        "-   Python is an interpreted language. There is no compiler and the\n",
+        "    languages are dynamic\n",
+        "\n",
+        "-   C language, for instance, makes optimization at the compiler level\n",
+        "    (before execution) to speed up your code\n",
+        "\n",
+        "-   Thus, NumPy implements arrays in C, which speeds things up\n",
+        "\n",
+        "-   The other reason vectorization works in because of parallelization\n",
+        "\n",
+        "# Parallel Computing\n",
+        "\n",
+        "## Parallelization\n",
+        "\n",
+        "Compare the following codes. What are their run times?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "e1b91069",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def fib(n):\n",
+        "  if n <= 1:\n",
+        "    return n\n",
+        "  else:\n",
+        "    return fib(n - 1) + fib(n - 2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "18dfe855-fac7-4682-bd2b-65a3de0c7974",
+      "metadata": {},
+      "source": [
+        "## Parallelization"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "655a10db",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "import numpy\n",
+        "\n",
+        "def add_one(n, x):\n",
+        "  y = np.zeros(n)\n",
+        "  for i in range(n):\n",
+        "    y[i] = x[i] + 1\n",
+        "        \n",
+        "  return y"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "97f89aea-133d-4596-852b-e5b56963ae5b",
+      "metadata": {},
+      "source": [
+        "## Parallelization\n",
+        "\n",
+        "-   Both are $O(n)$, but the second code chunk can be done in *parallel*\n",
+        "    because the $n$ computations are independent.\n",
+        "\n",
+        "-   Fibonacci depends on the previous two values\n",
+        "\n",
+        "-   The requirements for code to the parallelized and vectorized are\n",
+        "    similar, but not the same\n",
+        "\n",
+        "-   The Numba library can help will parallelizing your code\n",
+        "\n",
+        "-   Note parallel means the process takes place on one machine, but\n",
+        "    *distributed* means the computation is shared across many machines\n",
+        "\n",
+        "# Greedy and Exhaustive Algorithms\n",
+        "\n",
+        "## Greedy Approach (literally)\n",
+        "\n",
+        "-   Let’s revisit the knapsack problem, taking a different approach.\n",
+        "\n",
+        "-   The items are:\n",
+        "\n",
+        "    -   Stereo: \\$3000, 4 kg\n",
+        "\n",
+        "    -   Laptop: \\$2000, 3 kg\n",
+        "\n",
+        "    -   Guitar: \\$1500, 1 kg\n",
+        "\n",
+        "-   If we follow the rule “get the most valuable item, then get second\n",
+        "    most valuable etc.” we would make \\$3000 by taking the stereo, which\n",
+        "    isn’t the optimal \\$3500\n",
+        "\n",
+        "-   A *greedy algorithm* picks the optimal move at each step, which\n",
+        "    hopefully leads to the overall optimal solution\n",
+        "\n",
+        "    -   But it finds the solution in $O(n)$ time\n",
+        "\n",
+        "## Greedy Apporach\n",
+        "\n",
+        "-   Let’s say you could take fractions of an item and we tried the\n",
+        "    greedy approach\n",
+        "\n",
+        "    -   Peanuts: \\$7/kg\n",
+        "\n",
+        "    -   Rice: \\$5/kg\n",
+        "\n",
+        "    -   Tea: \\$12/kg\n",
+        "\n",
+        "-   We would take tea until it runs out, followed by peanuts and rice.\n",
+        "    This is the optimal solution in $O(n)$ time!\n",
+        "\n",
+        "## Classroom Scheduling Problem\n",
+        "\n",
+        "-   Suppose we want to hold as many classes in a classroom as possible\n",
+        "    [1]\n",
+        "\n",
+        "| Class        | Start   | End     |\n",
+        "|--------------|---------|---------|\n",
+        "| Yoga         | 9AM     | 10AM    |\n",
+        "| Music Theory | 9:30AM  | 11AM    |\n",
+        "| Painting     | 10AM    | 11AM    |\n",
+        "| Algorithms   | 10:30AM | 11:30AM |\n",
+        "| Calculus     | 11AM    | 12PM    |\n",
+        "\n",
+        "2 Minutes: write down a greedy algorithm to solve this problem\n",
+        "\n",
+        "## Classroom Scheduling Problem\n",
+        "\n",
+        "Algorithm\n",
+        "\n",
+        "1.  Pick the class that ends the soonest. This is the first class you’ll\n",
+        "    hold in this classroom\n",
+        "\n",
+        "2.  Now, you have to pick a class that starts after the first class.\n",
+        "    Again, pick the class that ends the soonest. This is the second\n",
+        "    class you’ll hold\n",
+        "\n",
+        "3.  Repeat the second step\n",
+        "\n",
+        "This not only produces the correct solution but also does so in $O(n)$\n",
+        "time, for $n$ classes!\n",
+        "\n",
+        "## Classroom Scheduling Problem\n",
+        "\n",
+        "-   An alternative algorithm is the *exhaustive approach*\n",
+        "\n",
+        "    -   We try every combination of classes. At the end, we see which\n",
+        "        solution fits the most classes\n",
+        "\n",
+        "    -   We try every combination of items to steal. At the end, we see\n",
+        "        which solution has the most value\n",
+        "\n",
+        "-   While brute forcing might sound always unnecessary, there are cases\n",
+        "    where it is needed to get the optimal solution\n",
+        "\n",
+        "    -   When performing subset selection for regression or decision\n",
+        "        tree, we can’t guarantee the variables are uncorrelated. So\n",
+        "        forward/backward stepwise selection isn’t guaranteed to produce\n",
+        "        the best outcome\n",
+        "    -   More on this in a few slides\n",
+        "\n",
+        "-   2 minutes: what is the time complexity of best subset selection?\n",
+        "\n",
+        "## Greedy Approximation Algorithms\n",
+        "\n",
+        "-   Problems involving finding the best subset of a variable to max/min\n",
+        "    an objective value are generalized as the problem of finding the\n",
+        "    best *power set*.\n",
+        "\n",
+        "    -   There are $2^n$ power sets, which becomes impossible to\n",
+        "        calculate past $n=100$ (depending on the constants)\n",
+        "\n",
+        "-   *Approximation algorithms* are judged by how fast they are and how\n",
+        "    close they are to the optimal solution\n",
+        "\n",
+        "    -   Forward/backwards stepwise selection is an approximation\n",
+        "        algorithm to best subset selection\n",
+        "\n",
+        "## N-P Complete Problems\n",
+        "\n",
+        "-   In the power set problem, we need to brute force all combinations\n",
+        "    and test them. Such problems are called *N-P Complete*\n",
+        "\n",
+        "    -   A lot of smart people think it’s not possible to solve these\n",
+        "        with efficient algorithms\n",
+        "\n",
+        "-   It’s hard to tell if a problem is N-P complete\n",
+        "\n",
+        "    -   Finding the shortest path between two points is N-P complete\n",
+        "        (travelling salesman)\n",
+        "\n",
+        "    -   But the knapsack problem isn’t N-P complete because we can solve\n",
+        "        it using dynamic programming\n",
+        "\n",
+        "## Live Coding\n",
+        "\n",
+        "You are given an integer array prices where `prices[i]` is the price of\n",
+        "a given stock on the $i$th day.On each day, you may decide to buy and/or\n",
+        "sell the stock. You can only hold at most one share of the stock at any\n",
+        "time. However, you can buy it then immediately sell it on the same day.\n",
+        "\n",
+        "Find and return the maximum profit you can achieve.\n",
+        "\n",
+        "[1] From Bhargava chapter 8"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "e075d171",
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# INPUT\n",
+        "prices = [7,1,5,3,6,4]\n",
+        "# OUTPUT\n",
+        "7"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f5564358-201d-4342-960f-fd7b7a5228c9",
+      "metadata": {},
+      "source": [
+        "From\n",
+        "[leetcode](https://leetcode.com/problems/best-time-to-buy-and-sell-stock-ii/description/)\n",
+        "\n",
+        "# Faster Implementations versus Faster Algorithms\n",
+        "\n",
+        "## Faster Implementations versus Faster Algorithms\n",
+        "\n",
+        "-   There are two ways we speed up our code\n",
+        "\n",
+        "    -   Use a faster algorithm, such as dynamic programming instead of\n",
+        "        brute force. Algorithms are concerned with the approach to the\n",
+        "        problem\n",
+        "\n",
+        "    -   Use a faster implementation, such as vectorization instead of\n",
+        "        loops\n",
+        "\n",
+        "-   It is useful to think about these separately when developing a\n",
+        "    programming, then combining them to create a super-fast approach!\n",
+        "\n",
+        "# Recommended Problems and References\n",
+        "\n",
+        "## Recommended Problems and Readings\n",
+        "\n",
+        "-   Cormen: Chapter 34 on NP-Completeness (highly optional)\n",
+        "\n",
+        "-   Bhargava: Chapter 8 exercises\n",
+        "\n",
+        "    -   8.1 - 8.8\n",
+        "\n",
+        "-   Vectorize the second code chunk in the Parallelization section\n",
+        "\n",
+        "-   [Find the longest palindrome from a\n",
+        "    string](https://leetcode.com/problems/longest-palindrome/) Hint: use\n",
+        "    a greedy alogrithm\n",
+        "\n",
+        "-   [Computing Pascal’s\n",
+        "    triangle](https://leetcode.com/problems/pascals-triangle/) Hint: use\n",
+        "    dynamic programming\n",
+        "\n",
+        "## References\n",
+        "\n",
+        "-   Bhargava, A. Y. (2016). *Grokking algorithms: An illustrated guide\n",
+        "    for programmers and other curious people.* Manning. Chapter 1.\n",
+        "\n",
+        "-   Cormen, T. H. (Ed.). (2009). *Introduction to algorithms* (3rd ed).\n",
+        "    MIT Press. Chapter 1 and 3."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": "3"
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.11.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
diff --git a/README.md b/README.md
index db202aa..fecb490 100644
--- a/README.md
+++ b/README.md
@@ -1,23 +1,37 @@
-# Algorithms and data structures
-
-## Contents:
-1. [Description](https://github.com/UofT-DSI/algorithms_and_data_structures#description)
-2. [Learning Outcomes](https://github.com/UofT-DSI/algorithms_and_data_structures#learning-outcomes)
-3. [Logistics](https://github.com/UofT-DSI/algorithms_and_data_structures#logistics)
-4. [Marking Scheme](https://github.com/UofT-DSI/algorithms_and_data_structures#marking-scheme)
-5. [Policies](https://github.com/UofT-DSI/algorithms_and_data_structures#policies)
-6. [Folder Structure](https://github.com/UofT-DSI/algorithms_and_data_structures#folder-structure)
-7. [Acknowledgements and Contributions](https://github.com/UofT-DSI/algorithms_and_data_structures#acknowledgements-and-contributions)
-
-## Description:
-The course was created by the University of Toronto's Data Science Institute. An understanding of data structures and algorithms (DSA), will aid the implementation of data science or machine learning methods in practice. Machine learning places emphasis on prediction, scalability, and autonomy. Understanding DSA is essential to the latter two aims of ML. For instance, students will be able to describe how an algorithms will perform when scaled or find practical methods for computers to solve problems autonomously. Finally, the industry often requires knowledge on DSAs and the ability to communicate the solving process. This course will provide the knowledge and terminology necessary to succeed in these situations.
-
-The beginning of the course will introduce students to terminology to discuss algorithms. This includes Big-O notation, time and space complexity. The next section will explore array-based data structures, searching, and sorting. Students should be able to justify algorithm or data structure choices based off time and space complexity analysis. Then, students will be introduced to recursion. We will solve problems using recursion and implment data structures that are best understood from a recursive perspective. Again, students will justify their design choices. The last portion of the course will be dedicated to solving optimization problems quickly. Students will be introduced to a variety of techniques to solve problems, identify when and how a solution can be optimized.
+# Algorithms and Data Structures
+
+## Content
+* [Description](#description)
+* [Learning Outcomes](#learning-outcomes)
+* [Logistics](#logistics)
+  + [Course Contacts](#course-contacts)
+  + [Delivery instructions](#delivery-instructions)
+* [Delivery of Module](#delivery-of-module)
+  + [How the Technical Facilitator will deliver](#how-the-technical-facilitator-will-deliver)
+  + [Expectations](#expectations)
+  + [Requirements](#requirements)
+  + [Lesson Schedule](#lesson-schedule)
+  + [Textbooks](#textbooks)
+* [Marking Scheme](#marking-scheme)
+* [Resources](#resources)
+  + [Documents](#documents)
+  + [Videos](#videos)
+  + [How to get help](#how-to-get-help)
+* [Folder Structure](#folder-structure)
+* [Acknowledgements and Contributions](#acknowledgements-and-contributions)
+* [Achnowledgements](#achnowledgements)
+  + [Contributions ](#contributions)
+
+## Description
+The course was created by the University of Toronto's Data Science Institute. An understanding of data structures and algorithms (DSA), will aid the implementation of data science or machine learning methods in practice. Machine learning emphasizes prediction, scalability, and autonomy. Understanding DSA is essential to the latter two aims of ML. For instance, students will be able to describe how algorithms will perform when scaled or find practical methods for computers to solve problems autonomously. Finally, the industry often requires knowledge of DSAs and the ability to communicate the solving process. This course will provide the knowledge and terminology necessary to succeed in these situations.
+
+The beginning of the course will introduce students to terminology to discuss algorithms. This includes Big-O notation, time and space complexity. The next section will explore array-based data structures, searching, and sorting. Students should be able to justify algorithm or data structure choices based on time and space complexity analysis. Then, students will be introduced to recursion. We will solve problems using recursion and implement data structures that are best understood from a recursive perspective. Again, students will justify their design choices. The last portion of the course will be dedicated to solving optimization problems quickly. Students will be introduced to a variety of techniques to solve problems and identify when and how a solution can be optimized.
 
 This course is designed for those who have a degree in something other than Computer Science/Statistics who are looking to enhance their data science skills for their career.
 
 ## Learning Outcomes
-Students will know how to...
+By the end of the module, learners will:
+
 1. Assess options and choices around fundamental algorithms and data structures using Big-O notation.
 2. Develop comfort with recursive functions.
 3. Decide on appropriate data structures
@@ -27,21 +41,39 @@ Students will know how to...
 ## Logistics
 
 ### Course Contacts
-* Instructor: **Salaar Liaqat** [s.liaqat@mail.utoronto.ca](s.liaqat@mail.utoronto.ca)
-  * Please include the keyword "DSI Algorithms" in your emails and use professional language.
-* TA: **Jenny Du** [junni.du@mail.utoronto.ca](junni.du@mail.utoronto.ca)
-  * Please include the keyword "DSI Algorithms" in your emails and use professional language.
+**Questions can be submitted to the #questions channel on Slack**
+
+* Technical Facilitator: **{Name}** {Pronouns}. Emails to the Technical Facilitator can be sent to {first_name.last_name}@mail.utoronto.ca.
+* Learning Support Staff: **{Name}** {Pronouns}. Emails to the Technical Facilitator can be sent to {first_name.last_name}@mail.utoronto.ca.
 
 ### Delivery instructions
-The workshop will be held over three weeks, three days a week. Two of the three days will be 2-hours long and the last day will be 3-hours. Being mindful of online fatigue, there will be one break during each class where students are encouraged to stretch, grab a drink and snacks, or ask any additional questions.
+The workshop will be held over three weeks, three days a week. Two of the three days will be 2 hours long and the last day will be 3 hours. Being mindful of online fatigue, there will be one break during each class where students are encouraged to stretch, grab a drink and snacks, or ask any additional questions.
+
+There will be a live coding component in most classes. Students are expected to follow along with the coding and ask questions throughout.
+
+## Delivery of Module
+The module will run synchronously three times a week on Zoom. The first three days are used as "lectures" and will last a maximum of 3 hours. During this time, the Technical Facilitator will introduce the concepts for the week. The last day is used as an optional, asynchronous work period. The work periods will also last for up to 3 hours. During the work period, a Technical Facilitator or Learning Support staff will be present on Zoom to assist learners reach the intended learning outcomes.
+
+### How the Technical Facilitator will deliver
+The Technical Facilitators will introduce the concepts through a collaborative live coding session using the Python notebooks found under `/01_slides`. All Technical Facilitators will also upload any live coding files to this repository for any learners to revisit under `/live_code`.
 
-There will be a live coding component in most classes. Students are expected to follow along with the coding and ask questions throughout. 
+### Expectations
+Learners are encouraged to be active participants while coding and are encouraged to ask questions throughout the module.
 
-### Technology Requirements
-1. Camera is optional although highly encouraged. We understand that not everyone may have the space at home to have the camera on.
+### Requirements
+* Learners are not expected to have any coding experience, we designed the learning content for beginners.
+* Learners are encouraged to ask questions and collaborate with others to enhance learning.
+* Learners must have a computer and an internet connection to participate in online activities.
+* Learners must have VSCode installed with the following extensions: 
+    * [Jupyter](https://marketplace.visualstudio.com/items?itemName=ms-toolsai.jupyter)
+    * [Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python)
+* Learners must not use generative AI such as ChatGPT to generate code to complete assignments. It should be used as a supportive tool to seek out answers to questions you may have.
+* We expect learners to have completed the [onboarding repo](https://github.com/UofT-DSI/Onboarding/tree/tech-onboarding-docs).
+* Webcam is optional although highly encouraged. We understand that not everyone may have the space at home to have the camera on.
 
 
 ### Lesson Schedule
+
 | Lesson | Topic                                                       | Resources  |
 |--------|-------------------------------------------------------------|------------|
 | 1      | Motivation and Big-O Notation                               | [Slides](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/lessons/1_motivation-big-o.pdf) |
@@ -49,64 +81,83 @@ There will be a live coding component in most classes. Students are expected to
 | 3      | Recursion              				                             | [Slides](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/lessons/3_recursion.pdf) |
 | 4      | Recursion              				                             | [Slides](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/lessons/3_recursion.pdf) |
 | 5      | Recursive Data Structures                                   | [Slides](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/lessons/4_recursive-ds.pdf) |
-| 6      | Recursive Data Structures                                   | [Slides](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/lessons/4_recursive-ds.pdf) |
-| 7      | Optimization                                                | [Slides](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/lessons/5_optimization.pdf) |
-| 8      | Optimization                                                | [Slides](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/lessons/5_optimization.pdf) |
-| 9      | Why is my code slow?                                        | [Slides](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/lessons/6_slow-code.pdf) |
 
 ### Textbooks
 
-The course content, slides, and recommended problems follow these two textbooks. They are freely available online after a quick google search. 
-
-* Bhargava, A. Y. (2016). *Grokking algorithms: An illustrated guide for programmers and other curious people.* Manning.
+The course content, slides, and recommended problems follow these two textbooks. They are freely available online after a quick Google search. 
 
+* Bhargava, A. Y. (2016). *Grokking algorithms: An illustrated guide for programmers and other curious people.* Manning. ([link](https://www.manning.com/books/grokking-algorithms-second-edition))
   * This textbook is easy to understand and very accessible. We will go deeper than this text.
-  
 * Cormen, T. H. (Ed.). (2009). *Introduction to algorithms (3rd ed).* MIT Press.
-
   * We won't cover the majority of this textbook. Many topics are too advanced and it goes into a lot of detail. 
 
 ## Marking Scheme
-| Assessment       | Weight    | Description          | Due Date |
-|------------------|-----------|----------------------|----------|
-| [Assignment 1](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/assignments/assignment%201.md) |    40%    | DSA coding practice  | Sat April 6, 11:59pm |
-| [Assignment 2](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/assignments/assignment%202.md) |    60%    | mock interview       | Fri April 12, 11:59pm  |
-
-## Policies
-Students should be active participants while coding and are encouraged to ask questions throughout.
+| Assessment       | Description          | Due Date |
+|------------------|----------------------|----------|
+| [Assignment 1](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/assignments/assignment%201.md) | DSA coding practice  | TBD |
+| [Assignment 2](https://github.com/UofT-DSI/algorithms_and_data_structures/blob/main/assignments/assignment%202.md) | mock interview       | TBD |
 
 **How to submit assignments, late policy, academic integrity.**
-Please submit your assignment by uploading the PDFs to your google drive folder used for assignment submissions.
+Please submit your assignment by uploading the PDFs to your Google Drive folder used for assignment submissions.
 
-Late policy: Everyone has a 24 hours grace period available in total for both assignments. Meaning if you submit the first assignment 12 hours late, you can submit the second assignment 12 
-hours late with no penalty. Or if you submit the first assignment on time, you can submit the second assignment upto 24 hours late. After using your grace period time, there will be a 20% 
+Late policy: Everyone has a 24-hour grace period available in total for both assignments. Meaning if you submit the first assignment 12 hours late, you can submit the second assignment 12 
+hours late with no penalty. Or if you submit the first assignment on time, you can submit the second assignment up to 24 hours late. After using your grace period time, there will be a 20% 
 penalty for 24 hours, and a 100% penalty after that. 
 
-If there are any extenuating circumstances, pleaese contact the course instructor as soon as possible for accommodations. 
+If there are any extenuating circumstances, please contact the course Technical Facilitator as soon as possible for accommodations. 
 
-## Folder Structure
-Below are the folders contained in this repo with a description of what they contain and information on how to use them.
+## Resources
+Feel free to use the following as resources:
 
-### 1 *assignments*:
-This folder contains the assignments.
+### Documents
+- [Big O Cheatsheet](https://www.bigocheatsheet.com/)
+- [Sorting Cheatsheet](https://www.interviewcake.com/sorting-algorithm-cheat-sheet)
+- [Visual Go - Graph Traversal](https://visualgo.net/en/dfsbfs?slide=2)
+- [Codecademy Explanation](https://www.codecademy.com/article/tree-traversal)
 
-### 2. *homework*:
-This folder contains a pdf file with all the recommended problems. The problems are also found after each coresponding set of slides. 
+### Videos
+- [15 Sorting Algorithms in 6 minutes](https://www.youtube.com/watch?v=kPRA0W1kECg) (Warning: This video could cause seizures for people with photosensitive epilepsy)
+- [Big O Notation](https://www.youtube.com/watch?v=g2o22C3CRfU)
+- [Breadth-first Search in 4 minutes](https://www.youtube.com/watch?v=HZ5YTanv5QE)
+- [Depth-first Search in 4 minutes](https://www.youtube.com/watch?v=Urx87-NMm6c)
+- [Nearest Neighbour Algorithm](https://www.youtube.com/watch?v=zPgsNsOfxQ8)
+- [K-d Trees](https://www.youtube.com/watch?v=Glp7THUpGow)
+- [Sorting Playlist](https://www.youtube.com/playlist?list=PL9xmBV_5YoZOZSbGAXAPIq1BeUf4j20pl)
 
-Homework is just a suggestion but will help students throughout the workshop, as content is cumulative and will only get more difficult. Unfortunately, there is not enough time to review previous content each class so while this homework is **not** graded, it is highly recommended.
+### How to get help
+![image](/steps_to_ask_for_help.png)
 
-### 3. *lessons*:
-This folder contains the pdf version of the slides.
+<hr>
 
-pdf slides should be referenced before class to prepare or after class to review. They contain all information that was discussed in class and are a great resource in the future if students need to reassess their knowledge. At the end of each set of slides, there are recommended problems, readings, and additional resources should students desire to learn more. 
+## Folder Structure
 
-### 5. *slides-resources*:
-This folder contains all editable slides. The slides were created using Quarto. 
+```markdown
+.
+├── 01_slides
+├── 02_assignments
+├── 03_homework
+├── 04_instructors
+├── LICENSE
+├── README.md
+└── steps_to_ask_for_help.png
+```
+
+* **slides:** Course slides as interactive notebooks (`.ipynb` files)
+* **html slides:** Course slides as HTML files that can be downloaded and viewed in a web browser
+* **pdf slides:** Course slides as PDF files
+* **live_coding:** Notebooks from class live coding sessions
+* **homework:** Optional homework to practice concepts covered in class
+* **assignments:** Graded assignments
+* **instructors:** Instructions for the Technical Facilitator on what to teach
+* **additional resources:** Additional materials not covered by the module
+* README: This file!
+* .gitignore: Files to exclude from this folder, specified by the Technical Facilitator
 
 ## Acknowledgements and Contributions
-## Achnowledgements
-* The course was devloped by [Alex Yu](https://www.linkedin.com/in/kunzhi-yu/) under the supervision of Rohan Alexander. 
-* We wish to acknowledge this land on which the University of Toronto operates. For thousands of years it has been the traditional land of the Huron-Wendat, the Seneca, and most recently, the Mississaugas of the Credit River. Today, this meeting place is still the home to many Indigenous people from across Turtle Island and we are grateful to have the opportunity to work on this land.
+
+### Achnowledgements
+* The module was developed by [Alex](https://www.linkedin.com/in/kunzhi-yu/) Yu](https://www.linkedin.com/in/kunzhi-yu/) under the supervision of Rohan Alexander. 
+* We wish to acknowledge this land on which the University of Toronto operates. For thousands of years, it has been the traditional land of the Huron-Wendat, the Seneca, and most recently, the Mississaugas of the Credit River. Today, this meeting place is still the home to many Indigenous people from across Turtle Island and we are grateful to have the opportunity to work on this land.
 ### Contributions 
 * `algorithms_and_data_structures` welcomes issues, enhancement requests, and other contributions. To submit an issue, use the [GitHub
 issues](https://github.com/UofT-DSI/algorithms_and_data_structures/issues).
diff --git a/assignments/.Rapp.history b/assignments/.Rapp.history
deleted file mode 100644
index e69de29..0000000
diff --git a/assignments/.Rhistory b/assignments/.Rhistory
deleted file mode 100644
index e69de29..0000000
diff --git a/assignments/assignment 1.md b/assignments/assignment 1.md
deleted file mode 100644
index 394b3b0..0000000
--- a/assignments/assignment 1.md	
+++ /dev/null
@@ -1,56 +0,0 @@
-# Coding Problems
-
-## Objective
-
-This assignment aims to demonstrate how to study a data structures or algorithms question in depth to prepare for an industry coding interview. Leetcode is a popular coding practice site that many use to practice for technical interviews. Like behavioral interviews, it's important to practice and keep your skills sharp.
-
-## Group Size
-
-Please complete this individually.
-
-## Outline
-
-### Part 1:
-
-You will be assigned one of three problems based of your first name. Excute "(hash('your first name')%3)+1" in python, and that will tell you your assigned problem. Include this line of code in your submitted notebook pdf. You can find the problem description in q[assigned number].md. They are based-off problems from Leetcode. 
-
-### Part 2:
-
-In a Jupyter Notebook (.ipynb) file, create 6 headings are write down the following:
-
--   Paraphrase the problem in your own words
-
--   In the .md file containing your problem, there are examples that illustrate how the code should work. Create 2 new examples that demonstrate you understand the problem.
-
--   Code the solution to your assigned problem in Python (code chunk). Try to find the best time and space complexity solution!
-
--   Explain why your solution works
-
--   Explain the problem’s and space complexity
-
--   Explain the thinking to an alternative solution (no coding required, but a classmate reading this should be able to code it up based off your text)
-
-Alternatively, you may use a Quarto file, or Word document with code screenshotted. However, we highly recommended you learn how to use a Jupyter Notebooks.
-
-Export each .ipynb file as a pdf. There are online converters, you can use the print option, or Google Collab has a printing feature. Please ensure all code and text is visible.
-
-## Submission Requirements
-
-Submit to your Google Drive folder with the following:
-
--   The PDF of the problem you have solved
-
-##Evaluation Criteria
-
--   Problem is accurately stated in the student’s own words
-
--   Two examples are correct and easily understandable
-
--   Correctness, time, and space complexity of the coding solution
-
--   Clarity in explaining why the solution works, its time and space complexity
-
--   Clarity in the proposal to the alternative solution
-
-## Submission Deadline
-Tuesday Feb 20, 2024 at 11:59pm
diff --git a/assignments/assignment 2.md b/assignments/assignment 2.md
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--- a/assignments/assignment 2.md	
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-# Practice Interview
-
-## Objective
-
-The partner assignment aims to provide students with the opportunity to practice coding in an interview context. You will analyze your partner's Assignment 1. Moreover, code reviews are common practice in a software development team. This assignment should give you a taste of the code review process.
-
-## Group Size
-
-Each group should have 2 people. You will be emailed details about your assigned partner shortly after submitting Assignment 1. 
-
-## Outline
-
-### Part 1:
-
-You and your partner should send to each other your Assignment 1 submission.
-
-
-### Part 2:
-
-Create a Jupyter Notebook, create 6 of the following headings, and complete the following about the your partner's assignment 1:
-
--   Paraphrase the problem in your own words.
-
--   Create 1 new example that demonstrates you understand the problem. Trace/walkthrough 1 example that your partner made and explain it.
-
--   Copy the solution your partner wrote. 
-
--   Explain why their solution works in your own words.
-
--   Explain the problem’s time and space complexity in your own words.
-
--   Critique your partner's solution, including explanation, if there is anything should be adjusted.
-
-### Part 3:
-
-Please write a 200 word reflection documenting your studying process from assignment 1, and your presentation and reviewing experience with your partner at the bottom of the Juypter Notebook under a new heading "Reflection." Again, export this Notebook as pdf.
-
-## Submission Requirements 
-
-In the same Google Drive folder you submitted assignment 1, please ADD the following
-
--   The PDF of the Jupyter Notebook you created for Assignment 2.
-
-Please name your files appropriately!
-
-## Evaluation Criteria
-
-We are looking for the similar points as Assignment 1
-
--   Problem is accurately stated in the student’s own words
-
--   New example is correct and easily understandable
-
--   Correctness, time, and space complexity of the coding solution
-
--   Clarity in explaining why the solution works, its time and space complexity
-
--   Quality of critique of your partner's assignment, if necessary
-
-## Submission Deadline
-Monday Feb 26, 2024 at 11:59pm
diff --git a/assignments/q1.md b/assignments/q1.md
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--- a/assignments/q1.md
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-# Question One: Check Duplicates in Tree
-
-Given the `root` of a binary tree, check whether it is contains a duplicate value. If a duplicate exists, return the duplicate value. If there are multiple duplicates, return the one with the closest distance to the root. If no duplicate exists, return -1.
-
-## Examples
-
-### Example 1
-
-![](images/q1_ex1.png "Example 1")
-
-Input: `root = [1, 2, 2, 3, 5, 6, 7]` *What traversal method is this?*
-
-Output: 2
-
-### Example 2
-
-![](images/q1_ex2.png "Example 2")
-
-Input: `root = [1, 10, 2, 3, 10, 12, 12]`
-
-Output: 10
-
-### Example 3
-
-![](images/q1_ex3.png "Example 3")
-
-Input: `root = [10, 9, 8, 7]`
-
-Output: -1
-
-## Starter Code
-
-```
-# Definition for a binary tree node.
-# class TreeNode(object):
-#     def __init__(self, val = 0, left = None, right = None):
-#         self.val = val
-#         self.left = left
-#         self.right = right
-def is_symmetric(root: TreeNode) -> int:
-    # TODO
-```
diff --git a/assignments/q2.md b/assignments/q2.md
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--- a/assignments/q2.md
+++ /dev/null
@@ -1,34 +0,0 @@
-# Question Two: Path to Leaves
-
-Given the `root` of a binary tree, return all root to leaf paths in any order.
-
-## Examples
-
-### Example 1
-
-![](images/q1_ex1.png "Example 1")
-
-Input: `root = [1, 2, 2, 3, 5, 6, 7]` *What traversal method is this?*
-
-Output: [[1, 2, 3], [1, 2, 5], [1, 2, 6], [1, 2, 7]]
-
-### Example 2
-
-![](images/q1_ex3.png "Example 2")
-
-Input: `root = [10, 9, 8, 7]`
-
-Output: [[10, 7], [10, 9, 8]]
-
-## Starter Code
-
-```
-# Definition for a binary tree node.
-# class TreeNode(object):
-#     def __init__(self, val = 0, left = None, right = None):
-#         self.val = val
-#         self.left = left
-#         self.right = right
-def bt_path(root: TreeNode) -> List[List[int]]:
-    # TODO
-```
diff --git a/assignments/q3.md b/assignments/q3.md
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index 9dffc12..0000000
--- a/assignments/q3.md
+++ /dev/null
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-# Question Three: Missing Number in Range
- 
-You are given a list containing `n` integers in the range `[0, n]`. Return a list of numbes that are missing from the range `[0, n]` of the array. If there is no missing number, return -1. Note, all the integers in the list may not be unique.
- 
-## Examples
-
-### Example 1
-
-Input: `lst = [0, 2]`
-
-Output: [1]
-
-### Example 2
-
-Input: `lst = [5, 0, 1]`
-
-Output: [2, 3, 4]
-
-### Example 3
-
-Input: `lst = [6, 8, 2, 3, 5, 7, 0, 1, 10]`
-
-Output: [4, 9]
-
-## Starter Code
-
-```
-def missing_num(nums: List) -> int:
-    # TODO
-```
diff --git a/homework/.Rhistory b/homework/.Rhistory
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diff --git a/homework/homework.pdf b/homework/homework.pdf
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diff --git a/homework/homework.qmd b/homework/homework.qmd
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----
-title: "Recommended Homework Problems"
-format: 
-  beamer:
-    institute: Data Sciences Institute, UofT
-    theme: Boadilla
-    colortheme: rose
-execute:
-  echo: true
-editor: visual
-author: Salaar Liaqat
----
-
-
-## 1: Motivation and Big-O
-
--   Cormen: Chapter 1 exercises
-
-    -   1.2-1, 1.2-2, 1.2-3
-
--   Bhargava: Chapter 1 exercises
-
-    -   1.3 to 1.5
-
--   Additional (for the mathematically inclined)
-
-    -   In CS, log is usually base 2, but a strong distinction is not made because *logs of different bases only differ by a constant factor* and constants are dropped in Big-O. Show this is true
-
-    -   Show that exponents of different bases **do not** differ by a constant factor
-
-## 2: Data Structures, Searching, and Sorting
-
--   Bhargava: Chapter 5
-
-    -   5.1 to 5.4
-
-
--   Give examples of 2 situations to use a queue and 2 situations to use a stack
-
--   In Python, code a `stack` class with `is_empty`, `push`, and `pop` methods using the end of a Python list as the top of the stack. Bonus: Compare the run time of using the start of the list versus the end of the list as the top of the stack using the `timeit` library!
-
--   In Python, code a `binary_search` function.
-
--   In Python, code a `hash_table` that can hash 4 values.
-
-## 3: Recusion
-
--   Bhargava: Chapter 4 exercises
-
-    -   4.1 to 4.8
-
--   Write a recursive function that produces the `RecursionError: maximum recursion depth exceeded` error.
-
--   Write a iterative function to calculate the $n$th Fibonacci number. What is its time and space complexity?
-
--   Write a recursive function to determine if a string is a palindrome. What is its time and space complexity?
-
--   Write a recursive function to check if a given positive integer is a prime number. What is its time and space complexity?
-
-## 3: Recusion
-
--   Suppose you have a plot of land and want to divide the land into even square plots, while keeping the plots as big as possible. How would you do this using D&C? See Bhargava pg. 52.
-
--   Explain why the "merge" step in mergesort is $O(n)$
-
--   Implement mergesort. You might find using helper functions useful.
-
--   Write a recursive function to perform binary search on a sorted list
-
-## 4: Recursive Data Structures
-
--   Cormen: Chapter 10 exercises
-
-    -   10.3-1, 10.3-2, 10.3-3
-
--   Bhargava: Chapter 6 exercises
-
-    -   6.1 to 6.5
-
-## 4: Recursive Data Structures
-
--   Implement preorder, postorder, and level order traversal. Determine the time and space complexity in each case
-
--   Implement a function that find an element in a BST and deletes it. The descendants of the deleted node are given to the deleted node's parent. (Hard)
-
--   Using the `graph` class from the slides, implement BST search such that it stops and tell you the distance the node is from the starting point.
-
-    -   For instance, if we searched for 7 in the graph given in the slides, it would return `"Found! Distance 2"`.
-
-    -   If we searched for 100 in the graph, it would return `"Not found!"`
-
--   Implement postorder graph traversal using the `graph` class from the slides.
-
-- Implement a function using recursion to find the sum heterogeneous nested lists such as [[1, [2]], [[[3]]], 4, [[5, 6], [[[7]]]]].
-
-
-## 5: Optimization
-
--   Bhargava: Chapter 9 exercises
-
-    -   9.1, 9.2
-
-    -   Read the knapsack problem FAQs on page 171
-
-    -   Follow the example about longest common substring on page 178
-
-## 5: Optimization
-
--   Write the code to brute force the diet problem. Compare the run times using the `timeit` library.
-
--   Modify the code from the slide such that there is an upper bound for calories and vitamins.
-
--   Page 17 of Bhargava covered the travelling sales person problem. Is it possible to improve the proposed solution using any method we learned today?
-
-
-## 6: Slow Code
-
--   Bhargava: Chapter 8 exercises
-
-    -   8.1 - 8.8
-
--   Vectorize the second code chunk in the Parallelization section of the lesson 6 slides
-
-- [Find the longest palindrome from a string](https://leetcode.com/problems/longest-palindrome/) Hint: use a greedy alogrithm
-
-- [Computing Pascal's triangle](https://leetcode.com/problems/pascals-triangle/) Hint: use dynamic programming
-
-## References
-
--   Bhargava, A. Y. (2016). *Grokking algorithms: An illustrated guide for programmers and other curious people.* Manning. Chapter 1.
-
--   Cormen, T. H. (Ed.). (2009). *Introduction to algorithms* (3rd ed). MIT Press. Chapter 1 and 3.
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diff --git a/slides-resources/1_motivation-big-o/.Rhistory b/slides-resources/1_motivation-big-o/.Rhistory
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--- a/slides-resources/1_motivation-big-o/.Rhistory
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@@ -1,512 +0,0 @@
-library(tidyverse)
-library(caret)
-knitr::opts_chunk$set(message = FALSE)
-set.seed(20231101)
-# train data
-x <- rnorm(n = 300, mean = 3, sd = 1)
-epsilon <- rnorm(n = 300)
-y <- 0.5 + 0.1 * x + 0.2 * x^2 + epsilon
-train <- data.frame(y, x, epsilon)
-# test data
-x <- rnorm(n = 300, mean = 3, sd = 1)
-epsilon <- rnorm(n = 300)
-y <- 0.5 + 0.1 * x + 0.2 * x^2 + epsilon
-test <- data.frame(y, x)
-x_grid <- seq(0, 6, by = 0.02)
-knn <- function(x, y, x_grid, k) {
-n <- length(x_grid)
-predictions <- rep(NA, n)
-for (i in 1:n) {
-differences <- abs(x - x_grid[i])
-nearest_indices <- order(differences)[1:k]
-predictions[i] <- mean(y[nearest_indices])
-}
-return(data.frame(x_grid, predictions))
-}
-ggplot() +
-geom_point(aes(x = x_grid, y = predictions, color = "blue"), data = knn(train$x, train$y, x_grid, 5)) +
-geom_point(aes(x = x, y = y, color = "red"), data = train) +
-scale_color_manual(labels = c("KNN k = 5", "Train Data"), values = c("blue", "red")) +
-theme_minimal()
-ggplot() +
-geom_point(aes(x = x_grid, y = predictions, color = "blue"), data = knn(train$x, train$y, x_grid, 20)) +
-geom_point(aes(x = x, y = y, color = "red"), data = train) +
-scale_color_manual(labels = c("KNN k = 20", "Train Data"), values = c("blue", "red")) +
-theme_minimal()
-ggplot() +
-geom_point(aes(x = x_grid, y = predictions, color = "blue"), data = knn(train$x, train$y, x_grid, 50)) +
-geom_point(aes(x = x, y = y, color = "red"), data = train) +
-scale_color_manual(labels = c("KNN k = 50", "Train Data"), values = c("blue", "red")) +
-theme_minimal()
-ggplot() +
-geom_point(aes(x = x_grid, y = predictions, color = "blue"), data = knn(train$x, train$y, x_grid, 100)) +
-geom_point(aes(x = x, y = y, color = "red"), data = train) +
-scale_color_manual(labels = c("KNN k = 100", "Train Data"), values = c("blue", "red")) +
-theme_minimal()
-w_tricubic <- function(x0, xi) {
-abs_x <- abs(x0 - xi)
-max_abs_x <- max(abs_x)
-return((1 - (abs_x / max_abs_x)^3)^3)
-}
-knn_weighted <- function(x, y, x_grid, k) {
-n <- length(x_grid)
-predictions <- rep(NA, n)
-for (i in 1:n) {
-differences <- abs(x - x_grid[i])
-nearest_indices <- order(differences)[1:k]
-weights <- w_tricubic(x_grid[i], x_grid[nearest_indices])
-predictions[i] <- sum(weights * y[nearest_indices]) / sum(weights)
-}
-return(data.frame(x_grid, predictions))
-}
-ggplot() +
-geom_point(aes(x = x_grid, y = predictions, color = "blue"), data = knn_weighted(train$x, train$y, x_grid, 5)) +
-geom_point(aes(x = x, y = y, color = "red"), data = train) +
-scale_color_manual(labels = c("KNN k = 5", "Train Data"), values = c("blue", "red")) +
-theme_minimal()
-ggplot() +
-geom_point(aes(x = x_grid, y = predictions, color = "blue"), data = knn_weighted(train$x, train$y, x_grid, 20)) +
-geom_point(aes(x = x, y = y, color = "red"), data = train) +
-scale_color_manual(labels = c("KNN k = 20", "Train Data"), values = c("blue", "red")) +
-theme_minimal()
-ggplot() +
-geom_point(aes(x = x_grid, y = predictions, color = "blue"), data = knn_weighted(train$x, train$y, x_grid, 50)) +
-geom_point(aes(x = x, y = y, color = "red"), data = train) +
-scale_color_manual(labels = c("KNN k = 50", "Train Data"), values = c("blue", "red")) +
-theme_minimal()
-ggplot() +
-geom_point(aes(x = x_grid, y = predictions, color = "blue"), data = knn_weighted(train$x, train$y, x_grid, 100)) +
-geom_point(aes(x = x, y = y, color = "red"), data = train) +
-scale_color_manual(labels = c("KNN k = 100", "Train Data"), values = c("blue", "red")) +
-theme_minimal()
-ggplot() +
-geom_point(aes(x = x_grid, y = predictions, color = "blue"), data = knn_linreg(train$x, train$y, x_grid, 5)) +
-geom_point(aes(x = x, y = y, color = "red"), data = train) +
-scale_color_manual(labels = c("KNN k = 5", "Train Data"), values = c("blue", "red")) +
-theme_minimal()
-knn_linreg <- function(x, y, x_grid, k) {
-n <- length(x_grid)
-predictions <- rep(NA, n)
-for (i in 1:n) {
-differences <- abs(x - x_grid[i])
-nearest_indices <- order(differences)[1:k]
-weights <- w_tricubic(x_grid[i], x_grid[nearest_indices])
-model <- lm(y[nearest_indices] ~ x[nearest_indices], weights = weights)
-predictions[i] <- predict(model, newdata = data.frame(x_train = x_grid[i]))
-}
-return(data.frame(x_grid, predictions))
-}
-ggplot() +
-geom_point(aes(x = x_grid, y = predictions, color = "blue"), data = knn_linreg(train$x, train$y, x_grid, 5)) +
-geom_point(aes(x = x, y = y, color = "red"), data = train) +
-scale_color_manual(labels = c("KNN k = 5", "Train Data"), values = c("blue", "red")) +
-theme_minimal()
-ggplot() +
-geom_point(aes(x = x_grid, y = predictions, color = "blue"), data = knn_linreg(train$x, train$y, x_grid, 20)) +
-geom_point(aes(x = x, y = y, color = "red"), data = train) +
-scale_color_manual(labels = c("KNN k = 20", "Train Data"), values = c("blue", "red")) +
-theme_minimal()
-ggplot() +
-geom_point(aes(x = x_grid, y = predictions, color = "blue"), data = knn_linreg(train$x, train$y, x_grid, 50)) +
-geom_point(aes(x = x, y = y, color = "red"), data = train) +
-scale_color_manual(labels = c("KNN k = 50", "Train Data"), values = c("blue", "red")) +
-theme_minimal()
-ggplot() +
-geom_point(aes(x = x_grid, y = predictions, color = "blue"), data = knn_linreg(train$x, train$y, x_grid, 100)) +
-geom_point(aes(x = x, y = y, color = "red"), data = train) +
-scale_color_manual(labels = c("KNN k = 100", "Train Data"), values = c("blue", "red")) +
-theme_minimal()
-mse <- function(y_pred, y){
-return(mean((y_pred-y)^2))
-}
-knn(train$x, train$y, x_grid, 5)
-mse(knn(train$x, train$y, x_grid, 5)[2], test$y)
-knn(train$x, train$y, x_grid, 5)[2]
-class(knn(train$x, train$y, x_grid, 5)[2])
-as.vector(knn(train$x, train$y, x_grid, 5)[2])
-mse(as.vector(knn(train$x, train$y, x_grid, 5)[2]), test$y)
-mse(c(1,2,3), c(1,1,1))
-mse(as.vector(knn(train$x, train$y, x_grid, 5)[2]), as.vector(test$y))
-test$y
-class(test$y)
-mse(knn(train$x, train$y, x_grid, 5)$predictions, as.vector(test$y))
-mse(knn(train$x, train$y, x_grid, 5)$predictions, as.vector(test$y))
-mse(knn(train$x, train$y, x_grid, 20)$predictions, as.vector(test$y))
-mse(knn(train$x, train$y, x_grid, 50)$predictions, as.vector(test$y))
-mse(knn(train$x, train$y, x_grid, 50)$predictions, as.vector(test$y))
-mse(knn(train$x, train$y, x_grid, 100)$predictions, as.vector(test$y))
-mse(knn_weighted(train$x, train$y, x_grid, 5)$predictions, as.vector(test$y))
-mse(knn_weighted(train$x, train$y, x_grid, 20)$predictions, as.vector(test$y))
-mse(knn_weighted(train$x, train$y, x_grid, 50)$predictions, as.vector(test$y))
-mse(knn_weighted(train$x, train$y, x_grid, 5)$predictions, as.vector(test$y))
-mse(knn_weighted(train$x, train$y, x_grid, 100)$predictions, as.vector(test$y))
-mse(knn_linreg(train$x, train$y, x_grid, 5)$predictions, as.vector(test$y))
-mse(knn_linreg(train$x, train$y, x_grid, 20)$predictions, as.vector(test$y))
-mse(knn_linreg(train$x, train$y, x_grid, 50)$predictions, as.vector(test$y))
-mse(knn_linreg(train$x, train$y, x_grid, 100)$predictions, as.vector(test$y))
-pi
-x <- c(1,2,3)
-x * -1
-?lgamma
-estimate_binom <- function(data) {
-n <- length(data)
-mean <- mean(data)
-theta_est <- 1 - (sum((data - mean) ^ 2) / sum(data))
-n_est <- mean / theta_est
-leave_one_out_theta <- rep(NA, n)
-leave_one_out_n <- rep(NA, n)
-for (i in 1:n) {
-data_i <- data[-i]
-leave_one_out_theta[i] <- 1 - (sum((data_i - mean(data_i)) ^ 2)  / sum(data_i))
-leave_one_out_n[i] <- mean(data_i) / leave_one_out_theta[i]
-}
-theta_std_err <- sqrt((sum(leave_one_out_theta - mean(leave_one_out_theta))^2) * (n - 1) / n)
-n_std_err <- sqrt((sum(leave_one_out_n - mean(leave_one_out_n))^2) * (n - 1) / n)
-return(list("theta estimate" = theta_est, "theta std err" = theta_std_err, "N estimate" = n_est, "N std err" = n_std_err))
-}
-estimate_binom(data)
-data <- scan("bugs.txt")
-estimate_binom <- function(data) {
-n <- length(data)
-mean <- mean(data)
-theta_est <- 1 - (sum((data - mean) ^ 2) / sum(data))
-n_est <- mean / theta_est
-leave_one_out_theta <- rep(NA, n)
-leave_one_out_n <- rep(NA, n)
-for (i in 1:n) {
-data_i <- data[-i]
-leave_one_out_theta[i] <- 1 - (sum((data_i - mean(data_i)) ^ 2)  / sum(data_i))
-leave_one_out_n[i] <- mean(data_i) / leave_one_out_theta[i]
-}
-theta_std_err <- sqrt((sum(leave_one_out_theta - mean(leave_one_out_theta))^2) * (n - 1) / n)
-n_std_err <- sqrt((sum(leave_one_out_n - mean(leave_one_out_n))^2) * (n - 1) / n)
-return(list("theta estimate" = theta_est, "theta std err" = theta_std_err, "N estimate" = n_est, "N std err" = n_std_err))
-}
-estimate_binom(data)
-max(x)
-LETTERS
-x[-2]
-estimate_binom <- function(data) {
-n <- length(data)
-theta_est <- 1 - (sum((data - mean(data)) ^ 2) / mean(data))
-n_est <- mean(data) / theta_est
-leave_one_out_theta <- rep(NA, n)
-leave_one_out_n <- rep(NA, n)
-for (i in 1:n) {
-leave_one_out_theta[i] <- 1 - (sum((data[-i] - mean(data[-i])) ^ 2)  / mean(data[-i]))
-leave_one_out_n[i] <- mean(data[-i]) / leave_one_out_theta[i]
-}
-theta_std_err <- sqrt((sum(leave_one_out_theta - mean(leave_one_out_theta))^2) * (n - 1) / n)
-n_std_err <- sqrt((sum(leave_one_out_n - mean(leave_one_out_n))^2) * (n - 1) / n)
-return(list("theta estimate" = theta_est, "theta std err" = theta_std_err, "N estimate" = n_est, "N std err" = n_std_err))
-}
-estimate_binom(data)
-estimate_binom <- function(data) {
-n <- length(data)
-theta_est <- 1 + sum(data) - (sum(data ^ 2) / sum(data))
-n_est <- mean(data) / theta_est
-leave_one_out_theta <- rep(NA, n)
-leave_one_out_n <- rep(NA, n)
-for (i in 1:n) {
-leave_one_out_theta[i] <- 1 + sum(data[-i]) - (sum(data[-i] ^ 2) / sum(data[-i]))
-leave_one_out_n[i] <- mean(data[-i]) / leave_one_out_theta[i]
-}
-theta_std_err <- sqrt((sum(leave_one_out_theta - mean(leave_one_out_theta))^2) * (n - 1) / n)
-n_std_err <- sqrt((sum(leave_one_out_n - mean(leave_one_out_n))^2) * (n - 1) / n)
-return(list("theta estimate" = theta_est, "theta std err" = theta_std_err, "N estimate" = n_est, "N std err" = n_std_err))
-}
-estimate_binom(data)
-estimate_binom <- function(data) {
-n <- length(data)
-theta_est <- 1 - (sum((data - mean(data)) ^ 2) / sum(data))
-n_est <- mean(data) / theta_est
-leave_one_out_theta <- rep(NA, n)
-leave_one_out_n <- rep(NA, n)
-for (i in 1:n) {
-leave_one_out_theta[i] <- 1 - (sum((data[-i] - mean(data[-i])) ^ 2)  / sum(data[-i]))
-leave_one_out_n[i] <- mean(data[-i]) / leave_one_out_theta[i]
-}
-theta_std_err <- sqrt((sum(leave_one_out_theta - mean(leave_one_out_theta))^2) * (n - 1) / n)
-n_std_err <- sqrt((sum(leave_one_out_n - mean(leave_one_out_n))^2) * (n - 1) / n)
-return(list("theta estimate" = theta_est, "theta std err" = theta_std_err, "N estimate" = n_est, "N std err" = n_std_err))
-}
-estimate_binom(data)
-?var
-muhat <- NULL
-sumx <- NULL
-for (i in 1:10000) { # computation of the Venter estimates and sample sums
-x <- rgamma(100,2)
-muhat <- c(muhat,venter(x,tau=1/2))
-sumx <- c(sumx,sum(x))
-}
-data <- scan("stamp.txt")
-venter <- function(x, tau=1/2) {
-x <- sort(x)
-n <- length(x)
-m <- ceiling(tau*n)
-x1 <- x[1:(n-m+1)]
-x2 <- x[m:n]
-j <- c(1:(n-m+1))
-len <- x2-x1
-k <- min(j[len==min(len)])
-(x[k]+x[k+m-1])/2
-}
-plot(density(data, bw = 0.0012))
-venter(data, 0.9)
-venter(data, 0.5)
-venter(data, 0.1)
-venter(data, 0.01)
-mse <- function(y_pred, y){
-return(mean((y_pred-y)^2))
-}
-mc_venter <- function(n, alpha, tau) {
-sim_data <- rgamma(n, alpha)
-return(venter(sim_data, tau))
-}
-set.seed(0)
-N <- 10000
-est_2_05_3 <- rep(NA, N)
-for (i in 1:N) {
-est_2_05_3[i] <- mc_venter(100, 2, 0.5)
-}
-mse_2_05_3 <- mse(est_2_05_3, 1)
-est_2_01_3 <- rep(NA, N)
-for (i in 1:N) {
-est_2_01_3 <- mc_venter(100, 2, 0.1)
-}
-mse_2_01_3 <- mse(est_2_01_3, 1)
-est_10_05_3 <- rep(NA, N)
-for (i in 1:N) {
-est_10_05_3 <- mc_venter(100, 10, 0.5)
-}
-mse_10_05_3 <- mse(est_10_05_3, 9)
-est_10_01_3 <- rep(NA, N)
-for (i in 1:N) {
-est_10_01_3 <- mc_venter(100, 10, 0.1)
-}
-mse_10_01_3 <- mse(est_10_01_3, 9)
-est_2_05_4 <- rep(NA, N)
-for (i in 1:N) {
-est_2_05_4[i] <- mc_venter(100, 2, 0.5)
-}
-mse_2_05_4 <- mse(est_2_05_4, 1)
-est_2_01_4 <- rep(NA, N)
-for (i in 1:N) {
-est_2_01_4 <- mc_venter(100, 2, 0.1)
-}
-mse_2_01_4 <- mse(est_2_01_4, 1)
-est_10_05_4 <- rep(NA, N)
-for (i in 1:N) {
-est_10_05_4 <- mc_venter(100, 10, 0.5)
-}
-mse_10_05_4 <- mse(est_10_05_4, 9)
-est_10_01_4 <- rep(NA, N)
-for (i in 1:N) {
-est_10_01_4 <- mc_venter(100, 10, 0.1)
-}
-mse_10_01_4 <- mse(est_10_01_4, 9)
-muhat <- NULL
-sumx <- NULL
-for (i in 1:10000) { # computation of the Venter estimates and sample sums
-x <- rgamma(100,2)
-muhat <- c(muhat,venter(x,tau=1/2))
-sumx <- c(sumx,sum(x))
-}
-y <- min(muhat) + (max(muhat)-min(muhat))*c(0:5000)/5000 # range of y values for f(y)
-fmuhat <- NULL
-for (z in y) { # calculate f(y) for each y value defined above
-fmuhat <- c(fmuhat,mean(dgamma(z,shape=200,scale=muhat/sumx)))
-}
-plot(y,fmuhat,type="l",ylab="density",lwd=3,col="red")
-source("~/.active-rstudio-document", echo=TRUE)
-source("~/.active-rstudio-document", echo=TRUE)
-# Example usage:
-set.seed(42)
-data <- rbinom(100, size = 1, prob = 0.3)  # Simulated binary data
-result <- estimate_binomial_parameters(data)
-cat("Method of Moments Estimation:\n")
-cat("p_mom =", result$p_mom, "\n")
-cat("q_mom =", result$q_mom, "\n")
-cat("Jackknife Standard Error Estimates:\n")
-cat("p_se =", result$p_se, "\n")
-cat("q_se =", result$q_se, "\n")
-# Function to estimate binomial parameters using Method of Moments and Jackknife
-estimate_binomial_parameters <- function(data) {
-n <- length(data)
-# Method of Moments Estimation
-p_mom <- mean(data) / length(data)
-q_mom <- 1 - p_mom
-# Jackknife Standard Error Estimation
-p_jack <- numeric(n)
-q_jack <- numeric(n)
-for (i in 1:n) {
-data_i <- data[-i]
-p_jack[i] <- mean(data_i) / (n - 1)
-q_jack[i] <- 1 - p_jack[i]
-}
-p_se <- sqrt((n - 1) / n * sum((p_jack - mean(p_jack))^2))
-q_se <- sqrt((n - 1) / n * sum((q_jack - mean(q_jack))^2))
-result <- list(
-"p_mom" = p_mom,
-"q_mom" = q_mom,
-"p_se" = p_se,
-"q_se" = q_se
-)
-return(result)
-}
-# Example usage:
-set.seed(42)
-data <- rbinom(100, size = 1, prob = 0.3)  # Simulated binary data
-result <- estimate_binomial_parameters(data)
-cat("Method of Moments Estimation:\n")
-cat("p_mom =", result$p_mom, "\n")
-cat("q_mom =", result$q_mom, "\n")
-cat("Jackknife Standard Error Estimates:\n")
-cat("p_se =", result$p_se, "\n")
-cat("q_se =", result$q_se, "\n")
-result
-estimate_binom <- function(data) {
-n <- length(data)
-n_est <- mean(data) ^ 2 / (sd(data)^2 + mean(data) - mean(data)^2)
-theta_est <- mean(data) / n_est
-leave_one_out_theta <- rep(NA, n)
-leave_one_out_n <- rep(NA, n)
-for (i in 1:n) {
-leave_one_out_n[i] <- mean(data[-i]) ^ 2 / (sd(data[-i])^2 + mean(data[-i]) - mean(data[-i])^2)
-leave_one_out_theta[i] <- mean(data[-i]) / leave_one_out_n[i]
-theta_std_err <- sqrt((sum(leave_one_out_theta - mean(leave_one_out_theta))^2) * (n - 1) / n)
-n_std_err <- sqrt((sum(leave_one_out_n - mean(leave_one_out_n))^2) * (n - 1) / n)
-return(list("theta estimate" = theta_est, "theta std err" = theta_std_err, "N estimate" = n_est, "N std err" = n_std_err))
-}
-estimate_binom <- function(data) {
-n <- length(data)
-n_est <- mean(data) ^ 2 / (sd(data)^2 + mean(data) - mean(data)^2)
-theta_est <- mean(data) / n_est
-leave_one_out_theta <- rep(NA, n)
-leave_one_out_n <- rep(NA, n)
-for (i in 1:n) {
-leave_one_out_n[i] <- mean(data[-i]) ^ 2 / (sd(data[-i])^2 + mean(data[-i]) - mean(data[-i])^2)
-leave_one_out_theta[i] <- mean(data[-i]) / leave_one_out_n[i]
-}
-theta_std_err <- sqrt((sum(leave_one_out_theta - mean(leave_one_out_theta))^2) * (n - 1) / n)
-n_std_err <- sqrt((sum(leave_one_out_n - mean(leave_one_out_n))^2) * (n - 1) / n)
-return(list("theta estimate" = theta_est, "theta std err" = theta_std_err, "N estimate" = n_est, "N std err" = n_std_err))
-}
-estimate_binom(data)
-estimate_binom <- function(data) {
-n <- length(data)
-n_est <- mean(data) ^ 2 / var(data) + mean(data) - mean(data)^2)
-estimate_binom <- function(data) {
-n <- length(data)
-n_est <- mean(data) ^ 2 / var(data) + mean(data) - mean(data)^2)
-estimate_binom <- function(data) {
-n <- length(data)
-n_est <- mean(data) ^ 2 / (var(data) + mean(data) - mean(data)^2)
-theta_est <- mean(data) / n_est
-leave_one_out_theta <- rep(NA, n)
-leave_one_out_n <- rep(NA, n)
-for (i in 1:n) {
-leave_one_out_n[i] <- mean(data[-i]) ^ 2 / (var(data[-i]) + mean(data[-i]) - mean(data[-i])^2)
-leave_one_out_theta[i] <- mean(data[-i]) / leave_one_out_n[i]
-}
-theta_std_err <- sqrt((sum(leave_one_out_theta - mean(leave_one_out_theta))^2) * (n - 1) / n)
-n_std_err <- sqrt((sum(leave_one_out_n - mean(leave_one_out_n))^2) * (n - 1) / n)
-return(list("theta estimate" = theta_est, "theta std err" = theta_std_err, "N estimate" = n_est, "N std err" = n_std_err))
-}
-estimate_binom(data)
-estimate_binom <- function(data) {
-n <- length(data)
-n_est <- mean(data) / (1 - var(data))
-theta_est <- mean(data) / n_est
-leave_one_out_theta <- rep(NA, n)
-leave_one_out_n <- rep(NA, n)
-for (i in 1:n) {
-leave_one_out_n[i] <- mean(data[-i]) / (1 - var(data[-i]))
-leave_one_out_theta[i] <- mean(data[-i]) / leave_one_out_n[i]
-}
-theta_std_err <- sqrt((sum(leave_one_out_theta - mean(leave_one_out_theta))^2) * (n - 1) / n)
-n_std_err <- sqrt((sum(leave_one_out_n - mean(leave_one_out_n))^2) * (n - 1) / n)
-return(list("theta estimate" = theta_est, "theta std err" = theta_std_err, "N estimate" = n_est, "N std err" = n_std_err))
-}
-estimate_binom(data)
-estimate_binom <- function(data) {
-n <- length(data)
-n_est <- mean(data) / (1 - var(data))
-theta_est <- mean(data) / n_est
-leave_one_out_mean <- rep(NA, n)
-leave_one_out_var <- rep(NA, n)
-for (i in 1:n) {
-leave_one_out_mean[i] <- mean(data[-i])
-leave_one_out_var[i] <- var(data[-i])
-}
-mean_n <- mean(leave_one_out_mean)
-mean_theta <- mean(leave_one_out_var)
-theta_std_err <- sqrt((sum(leave_one_out_theta - mean(leave_one_out_theta))^2) * (n - 1) / n)
-n_std_err <- sqrt((sum(leave_one_out_n - mean(leave_one_out_n))^2) * (n - 1) / n)
-return(list("theta estimate" = theta_est, "theta std err" = theta_std_err, "N estimate" = n_est, "N std err" = n_std_err))
-}
-estimate_binom(data)
-estimate_binom <- function(data) {
-n <- length(data)
-n_est <- mean(data) / (1 - var(data))
-theta_est <- mean(data) / n_est
-leave_one_out_theta <- rep(NA, n)
-leave_one_out_n <- rep(NA, n)
-for (i in 1:n) {
-leave_one_out_n[i] <- mean(data[-i]) / (1 - var(data[-i]))
-leave_one_out_theta[i] <- mean(data[-i]) / leave_one_out_n[i]
-}
-#mean_n <- mean(leave_one_out_mean)
-#mean_theta <- mean(leave_one_out_var)
-theta_std_err <- sqrt((sum(leave_one_out_theta - mean(leave_one_out_theta))^2) * (n - 1) / n)
-n_std_err <- sqrt((sum(leave_one_out_n - mean(leave_one_out_n))^2) * (n - 1) / n)
-return(list("theta estimate" = theta_est, "theta std err" = theta_std_err, "N estimate" = n_est, "N std err" = n_std_err))
-}
-estimate_binom(data)
-data <- scan("bugs.txt")
-estimate_binom <- function(data) {
-n <- length(data)
-theta_est <- 1 - (sum((data - mean(data)) ^ 2) / sum(data))
-n_est <- mean(data) / theta_est
-leave_one_out_theta <- rep(NA, n)
-leave_one_out_n <- rep(NA, n)
-for (i in 1:n) {
-leave_one_out_theta[i] <- 1 - (sum((data[-i] - mean(data[-i])) ^ 2)  / sum(data[-i]))
-leave_one_out_n[i] <- mean(data[-i]) / leave_one_out_theta[i]
-}
-theta_std_err <- sqrt((sum(leave_one_out_theta - mean(leave_one_out_theta))^2) * (n - 1) / n)
-n_std_err <- sqrt((sum(leave_one_out_n - mean(leave_one_out_n))^2) * (n - 1) / n)
-return(list("theta estimate" = theta_est, "theta std err" = theta_std_err, "N estimate" = n_est, "N std err" = n_std_err))
-}
-estimate_binom(data)
-data
-getwd()
-x <- "2023-11-27 09:50"
-y <- as.POSIXlt(x)
-rm(list = ls())  # remove the existing environment
-source("utils.R")
-source("discriminant_analysis.R")
-## Load the training and test data
-train <- Load_data("./data/digits_train.txt")
-test <- Load_data("./data/digits_test.txt")
-x_train <- train$x
-y_train <- train$y
-x_test <- test$x
-y_test <- test$y
-priors <- Comp_priors(y_train, 10)
-getwd()
-plot(rpois(1000, 10) + 18)
-plot(rpois(1000, 10) + 18)
-plot(dpois(1000, 10) + 18)
-plot(ppois(1000, 10) + 18)
-hist(rpois(1000, 10) + 19)
-hist(rpois(1000, 10) + 18)
-getwd
-getwd()
-source("~/.active-rstudio-document", echo=TRUE)
-source("~/.active-rstudio-document", echo=TRUE)
-source("~/.active-rstudio-document", echo=TRUE)
-reticulate::repl_python()
-quarto check
-reticulate::repl_python()
-reticulate::repl_python()
diff --git a/slides-resources/1_motivation-big-o/1_motivation-big-o.aux b/slides-resources/1_motivation-big-o/1_motivation-big-o.aux
deleted file mode 100644
index 2a35953..0000000
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+++ /dev/null
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----
-title: "Motivation and Big-O"
-format: 
-  beamer:
-    institute: Data Sciences Institute, UofT
-    theme: Boadilla
-    colortheme: rose
-execute:
-  echo: true
-editor: visual
-author: Salaar Liaqat
----
-
-## Outline
-
--   Motivation
-
--   Time Complexity: Introduction to Big-O Notation
-
--   Average, Best, and Worst Case
-
--   Space Complexity
-
-## About Me
-
--   PhD candidate in the Department of Computer Science at UofT
-
--   Thesis is on remote patient monitoring using wearables and mobile devices
-
--   Did BSc from Vancouver (SFU) and MSc from UofT
-
--   Co-Founder of a remote patient monitoring startup (Tabiat)
-
-## Why should a Data Scientist take this Course?
-
-- Problem solving. This course provides you with a framework to solve coding problems you may encounter in your career.
-
-- Efficient programs. We want to write programs that scale well with big data. 
-
-- Interview preparation. Many data science jobs require a technical interview, which involes solving algorithms problems. 
-
-
-## Learning Objectives
-
--   Assess options and choices around methods to solve problems and data representation methods using Big-O notation.
-
--   Develop comfort with recursive functions.
-
--   Decide on appropriate methods to represent data for a problem.
-
--   Take a client-led problem and translate it into an optimization problem.
-
--   Identify why code is running slowly and know how to improve its performance.
-
-
-# Motivating Code Demos
-
-## What are Algorithms and Data Structures
-
--   An **algorithm** is a procedure to solve a problem
-
-    -   Sort a data observations from smallest to largest
-
-    -   Find the nearest neighbor to a data point
-
-    -   How fast is each algorithm?
-
--   A **data structure** is a concrete method to store some data.
-
-    -   A pandas data set is a good way to store observations with many features.
-
-    -   How much space does the data sturcture need? How long does it take to access each observation?
-
-```{python}
-import numpy as np
-import timeit
-import random
-```
-
-## Loop Versus Vectorized Operations
-
-```{python}
-size = 10**4
-
-# Using Python lists
-list_a = list(range(size))
-list_b = list(range(size))
-
-# Using NumPy arrays
-array_a = np.arange(size)
-array_b = np.arange(size)
-
-# Timing for list addition
-list_time = timeit.timeit(lambda: 
-  [a + b for a, b in zip(list_a, list_b)], number=1)
-
-# Timing for vectorized array addition
-array_time = timeit.timeit(lambda: 
-  array_a + array_b, number = 1)
-```
-
-## Loop Versus Vectorized Operations
-
-We will learn about what vectorized is in lecture 6.
-
-```{python}
-print(f"List Addition: {list_time:.6f} seconds")
-print(f"Vectorized Addition: {array_time:.6f} seconds")
-```
-
--   Why was the NumPy vectorized operation much faster?
-
--   How can we describe how much faster the vectorized operation is?
-
--   This is useful in many iterative algorithms, such as gradient descent.
-
-## Search in List Versus Set
-
-We will learn about searching and sorting in lecture 2.
-
-```{python}
-# Python list
-list_time = timeit.timeit(lambda:
-  -1 in list_a, number = 1)
-  
-# Python set
-set_a = set(range(size))
-set_time = timeit.timeit(lambda:
-  -1 in set_a, number = 1)
-```
-
-## Search in List Versus Set
-
-```{python}
-print(f"List Search: {list_time:.6f} seconds")
-print(f"Set Search: {set_time:.6f} seconds")
-```
-
--   Why was the set search much faster?
-
--   How can we describe how much faster the vectorized operation is?
-
--   What are the pros and cons of choosing each data structure?
-
-## Selection Sort Versus Python Sort
-
-For context, selection sort is a naive sorting algorithm, while Python implements Tim Sort for the default search function.
-
-```{python}
-def selection_sort(arr):
-  n = len(arr)
-  
-  for i in range(n):
-    min_index = i
-    for j in range(i+1, n):
-      if arr[j] < arr[min_index]:
-        min_index = j
-        
-    arr[i], arr[min_index] = arr[min_index], arr[i]
-```
-
-## Selection Sort Versus Python Sort
-
-```{python}
-random.shuffle(list_a)
-rand_list = list_a.copy()
-
-sel_time = timeit.timeit(lambda:
-  selection_sort(rand_list.copy()), number = 1)
-  
-py_time = timeit.timeit(lambda:
-  sorted(rand_list.copy()), number = 1)
-```
-
-## Selection Sort Versus Python Sort
-
-```{python}
-print(f"Selection sort: {sel_time:.6f} seconds")
-print(f"Tim sort: {py_time:.6f} seconds")
-```
-
--   Why was selection sort much slower than Tim sort (not in detail)?
-
--   If we double the size of the list, how much slower will the code be in each case?
-
-# Time Complexity: Introduction to Big-O Notation
-
-## An example
-
--   Imagine you are writing an algorithm to search for a landing position for a rocket. You want it to be simple (to avoid bugs) and fast (since you only have 10 seconds to find a site). [^1]
-
--   It takes 1 millisecond to check each element. You decide to test a simple search and binary search on 100 elements (more on these methods later).
-
-    -   Simple search takes 100ms. Binary search takes 7ms.
-
--   Then you test binary search with 1 billion elements and it takes 32ms.
-
-    -   Binary search is about 15 times faster than simple search, because simple search took 100 ms with 100 elements, and binary search took 7 ms. So simple search will take 30 × 15 = 450ms with 1 billion elements.
-
--   Since that is within your threshold, you decide to go with simple search. **Is this correct?**
-
-[^1]: Example from Grokking Algorithms
-
-## A practical example
-
--   Definitely wrong!!
--   The run time of different algorithms can grow at different rates.
--   Big-O tells us how run time increases as the list size increases.
-
-### Comparing run times of simple and binary search
-
-| Elements      | Simple Search | Binary Search |
-|---------------|---------------|---------------|
-| 100           | 100 ms        | 7 ms          |
-| 10,000        | 10 s          | 14 ms         |
-| 1,000,000,000 | 11 days       | 32 ms         |
-
-## Big-O Notation
-
--   Big-O tells you how fast an algorithm is in terms of the number of operations, $n$.
-
--   Simple search needs to take each element, so it will take $n$ operations. The run time in Big-O notation is $O(n)$.
-
--   Binary search needs log $n$ operations, so the run time in Big-O notation is $O(\text{log}n)$
-
-    -   Note: log in computer science usually refers to log base 2.
-
-## Big-O is Upper Bound Run Time
-
--   Big-O notation is about the *worst-case* scenario.
-
-    -   If you were conducting linear search through a phone book, even if you were looking for Abe Aberdeen, it is still considered $O(n)$.
-
--   Formally, it characterizes an upper bound on the asymptotic behavior of the run time.
-
--   For example, the function $7n^3 + 30n^2 - 200n + 9$ has highest-order term $7n^3$. The function's growth rate is $n^3$ because the function grows no faster than $n^3$. The Big-O is $O(n^3)$.
-
-## Common Big-O Run Times
-
-Here are seven Big-O run times that you'll encounter frequently, sorted from fasted to slowest.
-
-::: columns
-::: {.column width="40%"}
-![](images/big-o-viz.jpg)
-:::
-
-::: {.column width="60%"}
--   $O(1)$, known as *constant time*. Ex: addition, division
-
--   $O(\text{log}n)$, known as *logarithmic time*. Ex: binary search
-
--   $O(n)$, known as *linear time*. Ex: Linear search
-
--   $O(n\text{log}n)$. Ex: Tim Sort
-
--   $O(n^2)$, known as *quadratic time*. Ex: Selection sort.
-
--   $O(2^n)$, known as *exponential time*. Ex: Naive recursive solution for nth Fibonacci number
-
--   $O(n!)$, known as *factorial time*. Ex. Traveling salesperson
-:::
-:::
-
-## Determining Time Complexity
-
-Consider the following code. How can you determine the Big-O?
-
-```{python}
-def f(n):
-  for i in range(n):
-    for j in range(n):
-      print(i, j)
-```
-
-## Determining Time Complexity
-
--   With "raw" Python code, you can usually count the number of nested `for` loops to determine the Big-O
-
-    -   A loop gives $O(n)$
-
-    -   A nested loop gives $O(n^2)$
-
--   It's usually not so simple in Data Science because of packages we use.
-
--   There are many factors affecting the constants in your run time
-
-    -   How complicated is each step? Is it $n$ or $2000n$?
-
-    -   How are your algorithms implemented? Is your programming language fast? Are your libraries fast?
-
--   Implementation issues will be covered later in the course.
-
-## Big-O with Two Variables
-
-Consider the following code. What is it's Big-O?
-
-```{python}
-def fun(n,m):
-  for i in range(n):
-    for j in range(m):
-      print("Hello")
-```
-
-## Big-O with Two Variables
-
--   The time complexity here is $O(nm)$.
-
-    -   If $n = m$, then $O(n^2)$.
-
--   All terms should be combined into one Big-O
-
-    -   $O(nm)$ is correct and $O(n)O(m)$ is incorrect.
-
-    -   $O(n + m)$ is correct and $O(n) + O(m)$ is incorrect.
-
-    -   $O(n^2 + mn + m)$ is written as $O(n^2 + nm)$. We can't throw away either term because we don't know which term will dominate.
-
--   Important to think about this when working with datasets.
-
-    -   They have $n$ rows and $p$ columns.
-
-    -   Can you reason how long it will take to fit a decision tree?
-
-# Best, Average, and Worst Case
-
-## Best, Average, and Worst Case
-
--   Big-O deals with worst case.
-
--   If we can develop a notion of an "average input," then we can devise the average case of an algorithm.
-
--   Best case is useful to think about the constants in your algorithm.
-
-    -   $O(\text{log}n)$ is always faster than $O(n)$ expect with very small $n$.
-
-# Space Complexity
-
-## What is Space Complexity
-
--   Aside from our algorithm taking too long to run, its also an issue if you run out of memory.
-
-    -   Note, memory (RAM), is not the same as disk space.
-
-    -   The computer will load data into memory from the disk
-
--   It will be problematic if you need to load 2 billion observations all at once.
-
--   We can also analyze space complexity with Big-O notation
-
--   Notice that time complexity is usually about the *algorithm*, while space complexity is about the *data structure*.
-
-## Examples
-
--   Code that prints `hello {your name}` will have $O(1)$ space.
-
--   Code that sums a list of size $n$ has $O(n)$ space.
-
--   You have users on Instagram, and you want to store who follows who. The answer depends (why?). The worst case space is $O(n^2)$
-
-# Recommended Problems and References
-
-## Recommended Problems
-
--   Cormen: Chapter 1 exercises
-
-    -   1.2-1, 1.2-2, 1.2-3
-
--   Bhargava: Chapter 1 exercises
-
-    -   1.3 to 1.5
-
--   Additional (for the mathematically inclined)
-
-    -   In CS, log is usually base 2, but a strong distinction is not made because *logs of different bases only differ by a constant factor* and constants are dropped in Big-O. Show this is true
-
-    -   Show that exponents of different bases **do not** differ by a constant factor
-
-## References
-
--   Bhargava, A. Y. (2016). *Grokking algorithms: An illustrated guide for programmers and other curious people.* Manning. Chapter 1.
-
--   Cormen, T. H. (Ed.). (2009). *Introduction to algorithms* (3rd ed). MIT Press. Chapter 1 and 3.
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-\author{Salaar Liaqat}
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-\institute{Data Sciences Institute, UofT}
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-\begin{frame}{Outline}
-\protect\hypertarget{outline}{}
-\begin{itemize}
-\item
-  Motivation
-\item
-  Time Complexity: Introduction to Big-O Notation
-\item
-  Average, Best, and Worst Case
-\item
-  Space Complexity
-\end{itemize}
-\end{frame}
-
-\begin{frame}{About Me}
-\protect\hypertarget{about-me}{}
-\begin{itemize}
-\item
-  PhD candidate in the Department of Computer Science at UofT
-\item
-  Thesis is on remote patient monitoring using wearables and mobile
-  devices
-\item
-  Did BSc from Vancouver (SFU) and MSc from UofT
-\item
-  Co-Founder of a remote patient monitoring startup (Tabiat)
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Why should a Data Scientist take this Course?}
-\protect\hypertarget{why-should-a-data-scientist-take-this-course}{}
-\begin{itemize}
-\item
-  Problem solving. This course provides you with a framework to solve
-  coding problems you may encounter in your career.
-\item
-  Efficient programs. We want to write programs that scale well with big
-  data.
-\item
-  Interview preparation. Many data science jobs require a technical
-  interview, which involes solving algorithms problems.
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Learning Objectives}
-\protect\hypertarget{learning-objectives}{}
-\begin{itemize}
-\item
-  Assess options and choices around methods to solve problems and data
-  representation methods using Big-O notation.
-\item
-  Develop comfort with recursive functions.
-\item
-  Decide on appropriate methods to represent data for a problem.
-\item
-  Take a client-led problem and translate it into an optimization
-  problem.
-\item
-  Identify why code is running slowly and know how to improve its
-  performance.
-\end{itemize}
-\end{frame}
-
-\hypertarget{motivating-code-demos}{%
-\section{Motivating Code Demos}\label{motivating-code-demos}}
-
-\begin{frame}[fragile]{What are Algorithms and Data Structures}
-\protect\hypertarget{what-are-algorithms-and-data-structures}{}
-\begin{itemize}
-\item
-  An \textbf{algorithm} is a procedure to solve a problem
-
-  \begin{itemize}
-  \item
-    Sort a data observations from smallest to largest
-  \item
-    Find the nearest neighbor to a data point
-  \item
-    How fast is each algorithm?
-  \end{itemize}
-\item
-  A \textbf{data structure} is a concrete method to store some data.
-
-  \begin{itemize}
-  \item
-    A pandas data set is a good way to store observations with many
-    features.
-  \item
-    How much space does the data sturcture need? How long does it take
-    to access each observation?
-  \end{itemize}
-\end{itemize}
-
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-
-\begin{frame}[fragile]{Loop Versus Vectorized Operations}
-\protect\hypertarget{loop-versus-vectorized-operations}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\NormalTok{size }\OperatorTok{=} \DecValTok{10}\OperatorTok{**}\DecValTok{4}
-
-\CommentTok{\# Using Python lists}
-\NormalTok{list\_a }\OperatorTok{=} \BuiltInTok{list}\NormalTok{(}\BuiltInTok{range}\NormalTok{(size))}
-\NormalTok{list\_b }\OperatorTok{=} \BuiltInTok{list}\NormalTok{(}\BuiltInTok{range}\NormalTok{(size))}
-
-\CommentTok{\# Using NumPy arrays}
-\NormalTok{array\_a }\OperatorTok{=}\NormalTok{ np.arange(size)}
-\NormalTok{array\_b }\OperatorTok{=}\NormalTok{ np.arange(size)}
-
-\CommentTok{\# Timing for list addition}
-\NormalTok{list\_time }\OperatorTok{=}\NormalTok{ timeit.timeit(}\KeywordTok{lambda}\NormalTok{: }
-\NormalTok{  [a }\OperatorTok{+}\NormalTok{ b }\ControlFlowTok{for}\NormalTok{ a, b }\KeywordTok{in} \BuiltInTok{zip}\NormalTok{(list\_a, list\_b)], number}\OperatorTok{=}\DecValTok{1}\NormalTok{)}
-
-\CommentTok{\# Timing for vectorized array addition}
-\NormalTok{array\_time }\OperatorTok{=}\NormalTok{ timeit.timeit(}\KeywordTok{lambda}\NormalTok{: }
-\NormalTok{  array\_a }\OperatorTok{+}\NormalTok{ array\_b, number }\OperatorTok{=} \DecValTok{1}\NormalTok{)}
-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}[fragile]{Loop Versus Vectorized Operations}
-\protect\hypertarget{loop-versus-vectorized-operations-1}{}
-We will learn about what vectorized is in lecture 6.
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"List Addition: }\SpecialCharTok{\{}\NormalTok{list\_time}\SpecialCharTok{:.6f\}}\SpecialStringTok{ seconds"}\NormalTok{)}
-\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Vectorized Addition: }\SpecialCharTok{\{}\NormalTok{array\_time}\SpecialCharTok{:.6f\}}\SpecialStringTok{ seconds"}\NormalTok{)}
-\end{Highlighting}
-\end{Shaded}
-
-\begin{verbatim}
-List Addition: 0.000337 seconds
-Vectorized Addition: 0.000052 seconds
-\end{verbatim}
-
-\begin{itemize}
-\item
-  Why was the NumPy vectorized operation much faster?
-\item
-  How can we describe how much faster the vectorized operation is?
-\item
-  This is useful in many iterative algorithms, such as gradient descent.
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Search in List Versus Set}
-\protect\hypertarget{search-in-list-versus-set}{}
-We will learn about searching and sorting in lecture 2.
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\CommentTok{\# Python list}
-\NormalTok{list\_time }\OperatorTok{=}\NormalTok{ timeit.timeit(}\KeywordTok{lambda}\NormalTok{:}
-  \OperatorTok{{-}}\DecValTok{1} \KeywordTok{in}\NormalTok{ list\_a, number }\OperatorTok{=} \DecValTok{1}\NormalTok{)}
-  
-\CommentTok{\# Python set}
-\NormalTok{set\_a }\OperatorTok{=} \BuiltInTok{set}\NormalTok{(}\BuiltInTok{range}\NormalTok{(size))}
-\NormalTok{set\_time }\OperatorTok{=}\NormalTok{ timeit.timeit(}\KeywordTok{lambda}\NormalTok{:}
-  \OperatorTok{{-}}\DecValTok{1} \KeywordTok{in}\NormalTok{ set\_a, number }\OperatorTok{=} \DecValTok{1}\NormalTok{)}
-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}[fragile]{Search in List Versus Set}
-\protect\hypertarget{search-in-list-versus-set-1}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"List Search: }\SpecialCharTok{\{}\NormalTok{list\_time}\SpecialCharTok{:.6f\}}\SpecialStringTok{ seconds"}\NormalTok{)}
-\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Set Search: }\SpecialCharTok{\{}\NormalTok{set\_time}\SpecialCharTok{:.6f\}}\SpecialStringTok{ seconds"}\NormalTok{)}
-\end{Highlighting}
-\end{Shaded}
-
-\begin{verbatim}
-List Search: 0.000036 seconds
-Set Search: 0.000000 seconds
-\end{verbatim}
-
-\begin{itemize}
-\item
-  Why was the set search much faster?
-\item
-  How can we describe how much faster the vectorized operation is?
-\item
-  What are the pros and cons of choosing each data structure?
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Selection Sort Versus Python Sort}
-\protect\hypertarget{selection-sort-versus-python-sort}{}
-For context, selection sort is a naive sorting algorithm, while Python
-implements Tim Sort for the default search function.
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\KeywordTok{def}\NormalTok{ selection\_sort(arr):}
-\NormalTok{  n }\OperatorTok{=} \BuiltInTok{len}\NormalTok{(arr)}
-  
-  \ControlFlowTok{for}\NormalTok{ i }\KeywordTok{in} \BuiltInTok{range}\NormalTok{(n):}
-\NormalTok{    min\_index }\OperatorTok{=}\NormalTok{ i}
-    \ControlFlowTok{for}\NormalTok{ j }\KeywordTok{in} \BuiltInTok{range}\NormalTok{(i}\OperatorTok{+}\DecValTok{1}\NormalTok{, n):}
-      \ControlFlowTok{if}\NormalTok{ arr[j] }\OperatorTok{\textless{}}\NormalTok{ arr[min\_index]:}
-\NormalTok{        min\_index }\OperatorTok{=}\NormalTok{ j}
-        
-\NormalTok{    arr[i], arr[min\_index] }\OperatorTok{=}\NormalTok{ arr[min\_index], arr[i]}
-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}[fragile]{Selection Sort Versus Python Sort}
-\protect\hypertarget{selection-sort-versus-python-sort-1}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\NormalTok{random.shuffle(list\_a)}
-\NormalTok{rand\_list }\OperatorTok{=}\NormalTok{ list\_a.copy()}
-
-\NormalTok{sel\_time }\OperatorTok{=}\NormalTok{ timeit.timeit(}\KeywordTok{lambda}\NormalTok{:}
-\NormalTok{  selection\_sort(rand\_list.copy()), number }\OperatorTok{=} \DecValTok{1}\NormalTok{)}
-  
-\NormalTok{py\_time }\OperatorTok{=}\NormalTok{ timeit.timeit(}\KeywordTok{lambda}\NormalTok{:}
-  \BuiltInTok{sorted}\NormalTok{(rand\_list.copy()), number }\OperatorTok{=} \DecValTok{1}\NormalTok{)}
-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}[fragile]{Selection Sort Versus Python Sort}
-\protect\hypertarget{selection-sort-versus-python-sort-2}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Selection sort: }\SpecialCharTok{\{}\NormalTok{sel\_time}\SpecialCharTok{:.6f\}}\SpecialStringTok{ seconds"}\NormalTok{)}
-\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Tim sort: }\SpecialCharTok{\{}\NormalTok{py\_time}\SpecialCharTok{:.6f\}}\SpecialStringTok{ seconds"}\NormalTok{)}
-\end{Highlighting}
-\end{Shaded}
-
-\begin{verbatim}
-Selection sort: 1.625933 seconds
-Tim sort: 0.000862 seconds
-\end{verbatim}
-
-\begin{itemize}
-\item
-  Why was selection sort much slower than Tim sort (not in detail)?
-\item
-  If we double the size of the list, how much slower will the code be in
-  each case?
-\end{itemize}
-\end{frame}
-
-\hypertarget{time-complexity-introduction-to-big-o-notation}{%
-\section{Time Complexity: Introduction to Big-O
-Notation}\label{time-complexity-introduction-to-big-o-notation}}
-
-\begin{frame}{An example}
-\protect\hypertarget{an-example}{}
-\begin{itemize}
-\item
-  Imagine you are writing an algorithm to search for a landing position
-  for a rocket. You want it to be simple (to avoid bugs) and fast (since
-  you only have 10 seconds to find a site). \footnote<.->{Example from
-    Grokking Algorithms}
-\item
-  It takes 1 millisecond to check each element. You decide to test a
-  simple search and binary search on 100 elements (more on these methods
-  later).
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Simple search takes 100ms. Binary search takes 7ms.
-  \end{itemize}
-\item
-  Then you test binary search with 1 billion elements and it takes 32ms.
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Binary search is about 15 times faster than simple search, because
-    simple search took 100 ms with 100 elements, and binary search took
-    7 ms. So simple search will take 30 × 15 = 450ms with 1 billion
-    elements.
-  \end{itemize}
-\item
-  Since that is within your threshold, you decide to go with simple
-  search. \textbf{Is this correct?}
-\end{itemize}
-\end{frame}
-
-\begin{frame}{A practical example}
-\protect\hypertarget{a-practical-example}{}
-\begin{itemize}
-\tightlist
-\item
-  Definitely wrong!!
-\item
-  The run time of different algorithms can grow at different rates.
-\item
-  Big-O tells us how run time increases as the list size increases.
-\end{itemize}
-
-\begin{block}{Comparing run times of simple and binary search}
-\protect\hypertarget{comparing-run-times-of-simple-and-binary-search}{}
-\begin{longtable}[]{@{}lll@{}}
-\toprule\noalign{}
-Elements & Simple Search & Binary Search \\
-\midrule\noalign{}
-\endhead
-100 & 100 ms & 7 ms \\
-10,000 & 10 s & 14 ms \\
-1,000,000,000 & 11 days & 32 ms \\
-\bottomrule\noalign{}
-\end{longtable}
-\end{block}
-\end{frame}
-
-\begin{frame}{Big-O Notation}
-\protect\hypertarget{big-o-notation}{}
-\begin{itemize}
-\item
-  Big-O tells you how fast an algorithm is in terms of the number of
-  operations, \(n\).
-\item
-  Simple search needs to take each element, so it will take \(n\)
-  operations. The run time in Big-O notation is \(O(n)\).
-\item
-  Binary search needs log \(n\) operations, so the run time in Big-O
-  notation is \(O(\text{log}n)\)
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Note: log in computer science usually refers to log base 2.
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Big-O is Upper Bound Run Time}
-\protect\hypertarget{big-o-is-upper-bound-run-time}{}
-\begin{itemize}
-\item
-  Big-O notation is about the \emph{worst-case} scenario.
-
-  \begin{itemize}
-  \tightlist
-  \item
-    If you were conducting linear search through a phone book, even if
-    you were looking for Abe Aberdeen, it is still considered \(O(n)\).
-  \end{itemize}
-\item
-  Formally, it characterizes an upper bound on the asymptotic behavior
-  of the run time.
-\item
-  For example, the function \(7n^3 + 30n^2 - 200n + 9\) has
-  highest-order term \(7n^3\). The function's growth rate is \(n^3\)
-  because the function grows no faster than \(n^3\). The Big-O is
-  \(O(n^3)\).
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Common Big-O Run Times}
-\protect\hypertarget{common-big-o-run-times}{}
-Here are seven Big-O run times that you'll encounter frequently, sorted
-from fasted to slowest.
-
-\begin{columns}[T]
-\begin{column}{0.4\textwidth}
-\includegraphics{images/big-o-viz.jpg}
-\end{column}
-
-\begin{column}{0.6\textwidth}
-\begin{itemize}
-\item
-  \(O(1)\), known as \emph{constant time}. Ex: addition, division
-\item
-  \(O(\text{log}n)\), known as \emph{logarithmic time}. Ex: binary
-  search
-\item
-  \(O(n)\), known as \emph{linear time}. Ex: Linear search
-\item
-  \(O(n\text{log}n)\). Ex: Tim Sort
-\item
-  \(O(n^2)\), known as \emph{quadratic time}. Ex: Selection sort.
-\item
-  \(O(2^n)\), known as \emph{exponential time}. Ex: Naive recursive
-  solution for nth Fibonacci number
-\item
-  \(O(n!)\), known as \emph{factorial time}. Ex. Traveling salesperson
-\end{itemize}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}[fragile]{Determining Time Complexity}
-\protect\hypertarget{determining-time-complexity}{}
-Consider the following code. How can you determine the Big-O?
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\KeywordTok{def}\NormalTok{ f(n):}
-  \ControlFlowTok{for}\NormalTok{ i }\KeywordTok{in} \BuiltInTok{range}\NormalTok{(n):}
-    \ControlFlowTok{for}\NormalTok{ j }\KeywordTok{in} \BuiltInTok{range}\NormalTok{(n):}
-      \BuiltInTok{print}\NormalTok{(i, j)}
-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}[fragile]{Determining Time Complexity}
-\protect\hypertarget{determining-time-complexity-1}{}
-\begin{itemize}
-\item
-  With ``raw'' Python code, you can usually count the number of nested
-  \texttt{for} loops to determine the Big-O
-
-  \begin{itemize}
-  \item
-    A loop gives \(O(n)\)
-  \item
-    A nested loop gives \(O(n^2)\)
-  \end{itemize}
-\item
-  It's usually not so simple in Data Science because of packages we use.
-\item
-  There are many factors affecting the constants in your run time
-
-  \begin{itemize}
-  \item
-    How complicated is each step? Is it \(n\) or \(2000n\)?
-  \item
-    How are your algorithms implemented? Is your programming language
-    fast? Are your libraries fast?
-  \end{itemize}
-\item
-  Implementation issues will be covered later in the course.
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Big-O with Two Variables}
-\protect\hypertarget{big-o-with-two-variables}{}
-Consider the following code. What is it's Big-O?
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\KeywordTok{def}\NormalTok{ fun(n,m):}
-  \ControlFlowTok{for}\NormalTok{ i }\KeywordTok{in} \BuiltInTok{range}\NormalTok{(n):}
-    \ControlFlowTok{for}\NormalTok{ j }\KeywordTok{in} \BuiltInTok{range}\NormalTok{(m):}
-      \BuiltInTok{print}\NormalTok{(}\StringTok{"Hello"}\NormalTok{)}
-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}{Big-O with Two Variables}
-\protect\hypertarget{big-o-with-two-variables-1}{}
-\begin{itemize}
-\item
-  The time complexity here is \(O(nm)\).
-
-  \begin{itemize}
-  \tightlist
-  \item
-    If \(n = m\), then \(O(n^2)\).
-  \end{itemize}
-\item
-  All terms should be combined into one Big-O
-
-  \begin{itemize}
-  \item
-    \(O(nm)\) is correct and \(O(n)O(m)\) is incorrect.
-  \item
-    \(O(n + m)\) is correct and \(O(n) + O(m)\) is incorrect.
-  \item
-    \(O(n^2 + mn + m)\) is written as \(O(n^2 + nm)\). We can't throw
-    away either term because we don't know which term will dominate.
-  \end{itemize}
-\item
-  Important to think about this when working with datasets.
-
-  \begin{itemize}
-  \item
-    They have \(n\) rows and \(p\) columns.
-  \item
-    Can you reason how long it will take to fit a decision tree?
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\hypertarget{best-average-and-worst-case}{%
-\section{Best, Average, and Worst
-Case}\label{best-average-and-worst-case}}
-
-\begin{frame}{Best, Average, and Worst Case}
-\protect\hypertarget{best-average-and-worst-case-1}{}
-\begin{itemize}
-\item
-  Big-O deals with worst case.
-\item
-  If we can develop a notion of an ``average input,'' then we can devise
-  the average case of an algorithm.
-\item
-  Best case is useful to think about the constants in your algorithm.
-
-  \begin{itemize}
-  \tightlist
-  \item
-    \(O(\text{log}n)\) is always faster than \(O(n)\) expect with very
-    small \(n\).
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\hypertarget{space-complexity}{%
-\section{Space Complexity}\label{space-complexity}}
-
-\begin{frame}{What is Space Complexity}
-\protect\hypertarget{what-is-space-complexity}{}
-\begin{itemize}
-\item
-  Aside from our algorithm taking too long to run, its also an issue if
-  you run out of memory.
-
-  \begin{itemize}
-  \item
-    Note, memory (RAM), is not the same as disk space.
-  \item
-    The computer will load data into memory from the disk
-  \end{itemize}
-\item
-  It will be problematic if you need to load 2 billion observations all
-  at once.
-\item
-  We can also analyze space complexity with Big-O notation
-\item
-  Notice that time complexity is usually about the \emph{algorithm},
-  while space complexity is about the \emph{data structure}.
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Examples}
-\protect\hypertarget{examples}{}
-\begin{itemize}
-\item
-  Code that prints \texttt{hello\ \{your\ name\}} will have \(O(1)\)
-  space.
-\item
-  Code that sums a list of size \(n\) has \(O(n)\) space.
-\item
-  You have users on Instagram, and you want to store who follows who.
-  The answer depends (why?). The worst case space is \(O(n^2)\)
-\end{itemize}
-\end{frame}
-
-\hypertarget{recommended-problems-and-references}{%
-\section{Recommended Problems and
-References}\label{recommended-problems-and-references}}
-
-\begin{frame}{Recommended Problems}
-\protect\hypertarget{recommended-problems}{}
-\begin{itemize}
-\item
-  Cormen: Chapter 1 exercises
-
-  \begin{itemize}
-  \tightlist
-  \item
-    1.2-1, 1.2-2, 1.2-3
-  \end{itemize}
-\item
-  Bhargava: Chapter 1 exercises
-
-  \begin{itemize}
-  \tightlist
-  \item
-    1.3 to 1.5
-  \end{itemize}
-\item
-  Additional (for the mathematically inclined)
-
-  \begin{itemize}
-  \item
-    In CS, log is usually base 2, but a strong distinction is not made
-    because \emph{logs of different bases only differ by a constant
-    factor} and constants are dropped in Big-O. Show this is true
-  \item
-    Show that exponents of different bases \textbf{do not} differ by a
-    constant factor
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}{References}
-\protect\hypertarget{references}{}
-\begin{itemize}
-\item
-  Bhargava, A. Y. (2016). \emph{Grokking algorithms: An illustrated
-  guide for programmers and other curious people.} Manning. Chapter 1.
-\item
-  Cormen, T. H. (Ed.). (2009). \emph{Introduction to algorithms} (3rd
-  ed). MIT Press. Chapter 1 and 3.
-\end{itemize}
-\end{frame}
-
-
-
-\end{document}
diff --git a/slides-resources/1_motivation-big-o/1_motivation-big-o.toc b/slides-resources/1_motivation-big-o/1_motivation-big-o.toc
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-\beamer@sectionintoc {1}{Motivating Code Demos}{6}{0}{1}
-\beamer@sectionintoc {2}{Time Complexity: Introduction to Big-O Notation}{15}{0}{2}
-\beamer@sectionintoc {3}{Best, Average, and Worst Case}{25}{0}{3}
-\beamer@sectionintoc {4}{Space Complexity}{27}{0}{4}
-\beamer@sectionintoc {5}{Recommended Problems and References}{30}{0}{5}
diff --git a/slides-resources/1_motivation-big-o/1_motivation-big-o.vrb b/slides-resources/1_motivation-big-o/1_motivation-big-o.vrb
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-\frametitle{Examples}
-\protect\hypertarget{examples}{}
-\begin{itemize}
-\item
-  Code that prints \texttt{hello\ \{your\ name\}} will have \(O(1)\)
-  space.
-\item
-  Code that sums a list of size \(n\) has \(O(n)\) space.
-\item
-  You have users on Instagram, and you want to store who follows who.
-  The answer depends (why?). The worst case space is \(O(n^2)\)
-\end{itemize}
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----
-title: "Array-Based Data Structures, Searching, and Sorting"
-format: 
-  beamer:
-    institute: Data Sciences Institute, UofT
-    theme: Boadilla
-    colortheme: rose
-execute:
-  echo: true
-editor: visual
-author: Salaar Liaqat
----
-
-## Outline
-
--   Array Based Data Structures
-
-    -   Stack, queue, Python List
-
--   Searching
-
-    -   Linear, Binary Search
-
--   Sorting
-
-    -   Selection, Insertion Sort
-
--   Hash map, hash table (Python dictionary), hash functions
-
-# Array Based Data Structures
-
-## Abstract Data Types Versus Data Structure
-
--   Some concepts are generally useful and transcend any programming language
-
--   An **abstract data type** (ADT) defines some kind of data and operations that can be performed on it
-
-    -   Abstract because there is no mention of *how* data is stored or *how* the operations work
-
-    -   Concerned about "what"
-
--   A **data structure** is a concrete method of storing data (and therefore its operations).
-
-    -   For instance, Python List is a data structure because it has a specific implementation.
-
--   ADTs form a common vocabulary for computer scientists to discuss problems. It allows us to focus on the design and worry about implementation later.
-
-## Important ADTs
-
--   Set
-
-    -   Data: a collection of unique elements
-
-    -   Operations: get size, insert a value (without introducing duplicates), remove a specified value, check membership
-
--   List
-
-    -   Data: an ordered sequence of elements
-
-    -   Operations: access element by index, insert a value at a given index, remove a value at a given index
-
-## Important ADTs [^1]
-
-[^1]: From https://www.teach.cs.toronto.edu/\~csc148h/winter/notes/
-
--   Map
-
-    -   Data: a collection of key-value pairs, where each key is unique and associated with a single value
-
-    -   Operations: look-up a value for a given key, insert a new key-value pair, remove a key-value pair, update the value associated with a given key
-
--   Iterable
-
-    -   Data: a collection of values (may or may not be unique)
-
-    -   Operations: iterate through the elements of the collection one at a time.
-
-## Relation between ADTs and Data Structures
-
--   A Python `list` is not a ADT. But it is a natural implementation of the List ADT.
-
-    -   The designers of Python implemented `list` operations
-
--   A single ADT can be implemented by many data structures
-
-    -   You could implement List ADT using a Python `dict`
-
-    -   We can store the list `["DS", 4, "Life"]` like this: `{0: "DS", 1: 4, 2: "Life"}`
-
--   A data structure can implement many ADTs
-
-    -   Practice: how can you implement a set with a Python `list`?
-
-## Python Lists
-
--   Each element has an address in memory. The addresses are ordered by index number and adjacent to each other.
-
--   Run time for `append` method
-
-    -   A new address is created and placed at the end of the list
-
-    -   $O(1)$ time because it doesn't matter how long the list is
-
--   Run time for `insert` method
-
-    -   The worst case occurs when you insert at the beginning of the list because each element in the list has to be shifted down by 1.
-
-    -   $O(n)$ time
-
--   Run time for `delete` method
-
-    -   If you remove the first element, all other elements must be shifted up by one.
-
-    -   $O(n)$ time
-
-## Stack
-
--   A stack contains zero or more items
-
-    -   Items are added at the top of the stack, called *pushing*
-
-    -   Items are removed from the top of the stack, called *popping*
-
--   The first item added to the stack is the last item removed
-
-    -   We call this "first-in-last-out" (LIFO) behavior
-
--   2 minutes: is it faster to use the front or back of a Python list to implement a stack? What is the Big-O for stack operations under each choice?
-
-## Queue
-
--   A queue contains zero or more items
-
-    -   Items are added at the rear of the queue, called *enqueue*
-
-    -   Items are removed from the front of the queue, called *dequeing*
-
--   Items come out of the queue in the order they were added
-
-    -   We call this "first-in-first-out" (FIFO) behavior
-
--   2 minutes: is it faster to use the front or back of a Python list to implement a queue? What is the Big-O for stack operations under each choice?
-
-# Searching
-
-## Motivating Example
-
--   You want to develop a ML method to search through a video to figure out when an bike is stolen.
-
--   You could start from the beginning of your video feed and run your ML method on each frame until you the bike is not in the frame.
-
-    -   This would take $O(n)$, probably a long time since you're using ML
-
--   What if we started halfway through? If the bike was there, then break the remaining video in half and check again. If the bike wasn't there, then break the previous part of the video in half and check again.
-
-    -   This is *binary search*
-
-## Binary Versus Linear Search
-
--   How many steps does binary searching through 100 numbers take? 10,000?
-
-    -   We can generalize this as $O(\text{log}n)$
-
--   What is the big-O of linear searching through 100 numbers? 10,000?
-
-    -   $O(n)$
-
--   Notice binary search requires the list to be sorted in advance.
-
-    -   We implicitly assumed this in the bike theft example (time is "sorted")
-
-# Sorting
-
-## Selection Sort
-
--   Suppose you want to sort prices of all fruits at a supermarket from lowest to highest
-
--   You go through the list, find the item with the lowest price then place it on top, then find the second lowest price and place it second, etc.
-
-    -   You will end up with a sorted list!
-
--   To find the lowest price, you need to traverse the entire list. You must do this $n$ times until there are no more items.
-
-    -   This takes $O(n^2)$ time
-
-## Insertion Sort
-
-::: columns
-::: {.column width="60%"}
--   Compare the current item to its predecessor. If the item is smaller than its predecessor, compare it to the items before. Move the greater items one position up to make space for the swapped item.
-
--   You need to traverse the list once for each item in the list, so the Big-O is $O(n^2)$.
-:::
-
-::: {.column width="40%"}
-![](images/insertion-sort.png)
-:::
-:::
-
-## Live Coding:
-
-The *h-index* is defined by Wikipedia as the maximum value of $h$ such that the given researcher has published at least $h$ papers that have each been cited at least $h$ times.
-
-Given a list of integers representing a researcher. Each index is their $ith$ publication and the value at the $ith$ index is the number of citation. Calculate the h-index of that researcher.
-
-### Example
-
-```{python}
-#| eval: false
-# INPUT
-lst = [2,2,5,6]
-# OUTPUT
-2
-```
-
-# Hash map, hash table (Python dictionary), hash functions
-
-## Motivating Example
-
--   Recall from the first lecture that searching in a Python set took (basically) 0 seconds
-
-    -   How was this achieved?
-
-    -   Binary search only has $O(\text{log}n)$ time, so there must be something else
-
--   To achieve $O(1)$ time, we need something that immediately knows the where/what the item is.
-
-    -   This is the purpose of *hash functions*
-
-## Hash Functions
-
--   A hash function is a function where you enter a string and it returns an integer
-
-    -   Python objects have hash
-
-```{python}
-hash("DS 4 Life")
-```
-
--   There are two requirements for a hash function
-
-    -   It needs to be consistent. For instance, if you enter "UofT" and get "1827", then every time you enter "UofT" you should get "1827"
-
-    -   It maps different words to different numbers. Each string has a unique hash.
-
-## Using Hash Functions: Example
-
--   Suppose you have a grocery store catalog with prices and barcodes. When you scan an item at checkout, you want it to instantly return the price.
-
--   You can put each barcode into a hash function.
-
-    -   Let's say barcode "1234" *hashes* to "1" and "2" hashes to "9876"
-
-    -   We store the price of item "1234" at address "1". Store the price of item "4321" at address "2"
-
-    -   We say the price at "1" is the *hash value* of "1"
-
--   If there are 8 items sold at the store, then the hash function will only return integers from 1 to 8
-
-    -   The size of the hash table is often referred to as its number of bins or slots.
-
-    -   Thus, the hash function depends on the array
-
--   This implementation is called a *hash table*
-
-    -   The hash table is basically a list of lists, and the hash function maps an object to its location in the outer list.
-
-## Python's Hash Tables: `dict`
-
--   You will likely never implement a hash table yourself, most languages have an implementation for has tables.
-
-    -   In Python, this is the `dict` class
-
--   Dictionaries have keys and values (barcodes and prices)
-
--   Dictionaries have really good performance. Search, insert, or delete item are all are $O(1)$ in the average case.
-
-    -   Average case assumes you have a "good" hash function that avoids *collisions*. You can read more about collisions in the textbooks.
-
-    -   The worst case of Python dictionaries for search, insert, and delete is $O(n)$.
-
--   Recall Python dictionaries don't allow duplicate keys, that is because has hashes must be unique!
-
-## Python `set`
-
--   Recall during the first lecture, we showcased that Python's set search was much faster than list search
-
--   This is because Python's set implements a hash function to store its values
-
-# Recommended Problems and References
-
-## Recommended Problems
-
--   Bhargava: Chapter 5
-
-    -   5.1 to 5.4
-
-    -   Read pages 79 to 86 on the use cases of hash functions
-
--   Additional
-
-    -   Give examples of 2 situations to use a queue and 2 situations to use a stack
-
-    -   In Python, code a `stack` class with `is_empty`, `push`, and `pop` methods using the end of a Python list as the top of the stack. Bonus: Compare the run time of using the start of the list versus the end of the list as the top of the stack using the `timeit` library!
-
-    -   In Python, code a `binary_search` function.
-
-    -   In Python, code a `hash_table` that can hash 4 values.
-
-## References
-
--   Bhargava, A. Y. (2016). *Grokking algorithms: An illustrated guide for programmers and other curious people.* Manning. Chapter 5.
-
--   Cormen, T. H. (Ed.). (2009). *Introduction to algorithms* (3rd ed). MIT Press. Chapter 2, 10, 11.
-
--   Horton, D., & Liu, D. (2023, November 19). *CSC148 Lecture Notes*. https://www.teach.cs.toronto.edu/\~csc148h/winter/notes/
diff --git a/slides-resources/2_ds-search-sort/2_ds-search-sort.snm b/slides-resources/2_ds-search-sort/2_ds-search-sort.snm
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-  Array Based Data Structures
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-Structures}\label{array-based-data-structures}}
-
-\begin{frame}{Abstract Data Types Versus Data Structure}
-\protect\hypertarget{abstract-data-types-versus-data-structure}{}
-\begin{itemize}
-\item
-  Some concepts are generally useful and transcend any programming
-  language
-\item
-  An \textbf{abstract data type} (ADT) defines some kind of data and
-  operations that can be performed on it
-
-  \begin{itemize}
-  \item
-    Abstract because there is no mention of \emph{how} data is stored or
-    \emph{how} the operations work
-  \item
-    Concerned about ``what''
-  \end{itemize}
-\item
-  A \textbf{data structure} is a concrete method of storing data (and
-  therefore its operations).
-
-  \begin{itemize}
-  \tightlist
-  \item
-    For instance, Python List is a data structure because it has a
-    specific implementation.
-  \end{itemize}
-\item
-  ADTs form a common vocabulary for computer scientists to discuss
-  problems. It allows us to focus on the design and worry about
-  implementation later.
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Important ADTs}
-\protect\hypertarget{important-adts}{}
-\begin{itemize}
-\item
-  Set
-
-  \begin{itemize}
-  \item
-    Data: a collection of unique elements
-  \item
-    Operations: get size, insert a value (without introducing
-    duplicates), remove a specified value, check membership
-  \end{itemize}
-\item
-  List
-
-  \begin{itemize}
-  \item
-    Data: an ordered sequence of elements
-  \item
-    Operations: access element by index, insert a value at a given
-    index, remove a value at a given index
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Important ADTs \footnote<.->{From
-  https://www.teach.cs.toronto.edu/\textasciitilde csc148h/winter/notes/}}
-\protect\hypertarget{important-adts-1}{}
-\begin{itemize}
-\item
-  Map
-
-  \begin{itemize}
-  \item
-    Data: a collection of key-value pairs, where each key is unique and
-    associated with a single value
-  \item
-    Operations: look-up a value for a given key, insert a new key-value
-    pair, remove a key-value pair, update the value associated with a
-    given key
-  \end{itemize}
-\item
-  Iterable
-
-  \begin{itemize}
-  \item
-    Data: a collection of values (may or may not be unique)
-  \item
-    Operations: iterate through the elements of the collection one at a
-    time.
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Relation between ADTs and Data Structures}
-\protect\hypertarget{relation-between-adts-and-data-structures}{}
-\begin{itemize}
-\item
-  A Python \texttt{list} is not a ADT. But it is a natural
-  implementation of the List ADT.
-
-  \begin{itemize}
-  \tightlist
-  \item
-    The designers of Python implemented \texttt{list} operations
-  \end{itemize}
-\item
-  A single ADT can be implemented by many data structures
-
-  \begin{itemize}
-  \item
-    You could implement List ADT using a Python \texttt{dict}
-  \item
-    We can store the list \texttt{{[}"DS",\ 4,\ "Life"{]}} like this:
-    \texttt{\{0:\ "DS",\ 1:\ 4,\ 2:\ "Life"\}}
-  \end{itemize}
-\item
-  A data structure can implement many ADTs
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Practice: how can you implement a set with a Python \texttt{list}?
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Python Lists}
-\protect\hypertarget{python-lists}{}
-\begin{itemize}
-\item
-  Each element has an address in memory. The addresses are ordered by
-  index number and adjacent to each other.
-\item
-  Run time for \texttt{append} method
-
-  \begin{itemize}
-  \item
-    A new address is created and placed at the end of the list
-  \item
-    \(O(1)\) time because it doesn't matter how long the list is
-  \end{itemize}
-\item
-  Run time for \texttt{insert} method
-
-  \begin{itemize}
-  \item
-    The worst case occurs when you insert at the beginning of the list
-    because each element in the list has to be shifted down by 1.
-  \item
-    \(O(n)\) time
-  \end{itemize}
-\item
-  Run time for \texttt{delete} method
-
-  \begin{itemize}
-  \item
-    If you remove the first element, all other elements must be shifted
-    up by one.
-  \item
-    \(O(n)\) time
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Stack}
-\protect\hypertarget{stack}{}
-\begin{itemize}
-\item
-  A stack contains zero or more items
-
-  \begin{itemize}
-  \item
-    Items are added at the top of the stack, called \emph{pushing}
-  \item
-    Items are removed from the top of the stack, called \emph{popping}
-  \end{itemize}
-\item
-  The first item added to the stack is the last item removed
-
-  \begin{itemize}
-  \tightlist
-  \item
-    We call this ``first-in-last-out'' (LIFO) behavior
-  \end{itemize}
-\item
-  2 minutes: is it faster to use the front or back of a Python list to
-  implement a stack? What is the Big-O for stack operations under each
-  choice?
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Queue}
-\protect\hypertarget{queue}{}
-\begin{itemize}
-\item
-  A queue contains zero or more items
-
-  \begin{itemize}
-  \item
-    Items are added at the rear of the queue, called \emph{enqueue}
-  \item
-    Items are removed from the front of the queue, called
-    \emph{dequeing}
-  \end{itemize}
-\item
-  Items come out of the queue in the order they were added
-
-  \begin{itemize}
-  \tightlist
-  \item
-    We call this ``first-in-first-out'' (FIFO) behavior
-  \end{itemize}
-\item
-  2 minutes: is it faster to use the front or back of a Python list to
-  implement a queue? What is the Big-O for stack operations under each
-  choice?
-\end{itemize}
-\end{frame}
-
-\hypertarget{searching}{%
-\section{Searching}\label{searching}}
-
-\begin{frame}{Motivating Example}
-\protect\hypertarget{motivating-example}{}
-\begin{itemize}
-\item
-  You want to develop a ML method to search through a video to figure
-  out when an bike is stolen.
-\item
-  You could start from the beginning of your video feed and run your ML
-  method on each frame until you the bike is not in the frame.
-
-  \begin{itemize}
-  \tightlist
-  \item
-    This would take \(O(n)\), probably a long time since you're using ML
-  \end{itemize}
-\item
-  What if we started halfway through? If the bike was there, then break
-  the remaining video in half and check again. If the bike wasn't there,
-  then break the previous part of the video in half and check again.
-
-  \begin{itemize}
-  \tightlist
-  \item
-    This is \emph{binary search}
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Binary Versus Linear Search}
-\protect\hypertarget{binary-versus-linear-search}{}
-\begin{itemize}
-\item
-  How many steps does binary searching through 100 numbers take? 10,000?
-
-  \begin{itemize}
-  \tightlist
-  \item
-    We can generalize this as \(O(\text{log}n)\)
-  \end{itemize}
-\item
-  What is the big-O of linear searching through 100 numbers? 10,000?
-
-  \begin{itemize}
-  \tightlist
-  \item
-    \(O(n)\)
-  \end{itemize}
-\item
-  Notice binary search requires the list to be sorted in advance.
-
-  \begin{itemize}
-  \tightlist
-  \item
-    We implicitly assumed this in the bike theft example (time is
-    ``sorted'')
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\hypertarget{sorting}{%
-\section{Sorting}\label{sorting}}
-
-\begin{frame}{Selection Sort}
-\protect\hypertarget{selection-sort}{}
-\begin{itemize}
-\item
-  Suppose you want to sort prices of all fruits at a supermarket from
-  lowest to highest
-\item
-  You go through the list, find the item with the lowest price then
-  place it on top, then find the second lowest price and place it
-  second, etc.
-
-  \begin{itemize}
-  \tightlist
-  \item
-    You will end up with a sorted list!
-  \end{itemize}
-\item
-  To find the lowest price, you need to traverse the entire list. You
-  must do this \(n\) times until there are no more items.
-
-  \begin{itemize}
-  \tightlist
-  \item
-    This takes \(O(n^2)\) time
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Insertion Sort}
-\protect\hypertarget{insertion-sort}{}
-\begin{columns}[T]
-\begin{column}{0.6\textwidth}
-\begin{itemize}
-\item
-  Compare the current item to its predecessor. If the item is smaller
-  than its predecessor, compare it to the items before. Move the greater
-  items one position up to make space for the swapped item.
-\item
-  You need to traverse the list once for each item in the list, so the
-  Big-O is \(O(n^2)\).
-\end{itemize}
-\end{column}
-
-\begin{column}{0.4\textwidth}
-\includegraphics{images/insertion-sort.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}[fragile]{Live Coding:}
-\protect\hypertarget{live-coding}{}
-The \emph{h-index} is defined by Wikipedia as the maximum value of \(h\)
-such that the given researcher has published at least \(h\) papers that
-have each been cited at least \(h\) times.
-
-Given a list of integers representing a researcher. Each index is their
-\(ith\) publication and the value at the \(ith\) index is the number of
-citation. Calculate the h-index of that researcher.
-
-\begin{block}{Example}
-\protect\hypertarget{example}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\CommentTok{\# INPUT}
-\NormalTok{lst }\OperatorTok{=}\NormalTok{ [}\DecValTok{2}\NormalTok{,}\DecValTok{2}\NormalTok{,}\DecValTok{5}\NormalTok{,}\DecValTok{6}\NormalTok{]}
-\CommentTok{\# OUTPUT}
-\DecValTok{2}
-\end{Highlighting}
-\end{Shaded}
-\end{block}
-\end{frame}
-
-\hypertarget{hash-map-hash-table-python-dictionary-hash-functions}{%
-\section{Hash map, hash table (Python dictionary), hash
-functions}\label{hash-map-hash-table-python-dictionary-hash-functions}}
-
-\begin{frame}{Motivating Example}
-\protect\hypertarget{motivating-example-1}{}
-\begin{itemize}
-\item
-  Recall from the first lecture that searching in a Python set took
-  (basically) 0 seconds
-
-  \begin{itemize}
-  \item
-    How was this achieved?
-  \item
-    Binary search only has \(O(\text{log}n)\) time, so there must be
-    something else
-  \end{itemize}
-\item
-  To achieve \(O(1)\) time, we need something that immediately knows the
-  where/what the item is.
-
-  \begin{itemize}
-  \tightlist
-  \item
-    This is the purpose of \emph{hash functions}
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Hash Functions}
-\protect\hypertarget{hash-functions}{}
-\begin{itemize}
-\item
-  A hash function is a function where you enter a string and it returns
-  an integer
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Python objects have hash
-  \end{itemize}
-\end{itemize}
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\BuiltInTok{hash}\NormalTok{(}\StringTok{"DS 4 Life"}\NormalTok{)}
-\end{Highlighting}
-\end{Shaded}
-
-\begin{verbatim}
--4234464436397453779
-\end{verbatim}
-
-\begin{itemize}
-\item
-  There are two requirements for a hash function
-
-  \begin{itemize}
-  \item
-    It needs to be consistent. For instance, if you enter ``UofT'' and
-    get ``1827'', then every time you enter ``UofT'' you should get
-    ``1827''
-  \item
-    It maps different words to different numbers. Each string has a
-    unique hash.
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Using Hash Functions: Example}
-\protect\hypertarget{using-hash-functions-example}{}
-\begin{itemize}
-\item
-  Suppose you have a grocery store catalog with prices and barcodes.
-  When you scan an item at checkout, you want it to instantly return the
-  price.
-\item
-  You can put each barcode into a hash function.
-
-  \begin{itemize}
-  \item
-    Let's say barcode ``1234'' \emph{hashes} to ``1'' and ``2'' hashes
-    to ``9876''
-  \item
-    We store the price of item ``1234'' at address ``1''. Store the
-    price of item ``4321'' at address ``2''
-  \item
-    We say the price at ``1'' is the \emph{hash value} of ``1''
-  \end{itemize}
-\item
-  If there are 8 items sold at the store, then the hash function will
-  only return integers from 1 to 8
-
-  \begin{itemize}
-  \item
-    The size of the hash table is often referred to as its number of
-    bins or slots.
-  \item
-    Thus, the hash function depends on the array
-  \end{itemize}
-\item
-  This implementation is called a \emph{hash table}
-
-  \begin{itemize}
-  \tightlist
-  \item
-    The hash table is basically a list of lists, and the hash function
-    maps an object to its location in the outer list.
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Python's Hash Tables: \texttt{dict}}
-\protect\hypertarget{pythons-hash-tables-dict}{}
-\begin{itemize}
-\item
-  You will likely never implement a hash table yourself, most languages
-  have an implementation for has tables.
-
-  \begin{itemize}
-  \tightlist
-  \item
-    In Python, this is the \texttt{dict} class
-  \end{itemize}
-\item
-  Dictionaries have keys and values (barcodes and prices)
-\item
-  Dictionaries have really good performance. Search, insert, or delete
-  item are all are \(O(1)\) in the average case.
-
-  \begin{itemize}
-  \item
-    Average case assumes you have a ``good'' hash function that avoids
-    \emph{collisions}. You can read more about collisions in the
-    textbooks.
-  \item
-    The worst case of Python dictionaries for search, insert, and delete
-    is \(O(n)\).
-  \end{itemize}
-\item
-  Recall Python dictionaries don't allow duplicate keys, that is because
-  has hashes must be unique!
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Python \texttt{set}}
-\protect\hypertarget{python-set}{}
-\begin{itemize}
-\item
-  Recall during the first lecture, we showcased that Python's set search
-  was much faster than list search
-\item
-  This is because Python's set implements a hash function to store its
-  values
-\end{itemize}
-\end{frame}
-
-\hypertarget{recommended-problems-and-references}{%
-\section{Recommended Problems and
-References}\label{recommended-problems-and-references}}
-
-\begin{frame}[fragile]{Recommended Problems}
-\protect\hypertarget{recommended-problems}{}
-\begin{itemize}
-\item
-  Bhargava: Chapter 5
-
-  \begin{itemize}
-  \item
-    5.1 to 5.4
-  \item
-    Read pages 79 to 86 on the use cases of hash functions
-  \end{itemize}
-\item
-  Additional
-
-  \begin{itemize}
-  \item
-    Give examples of 2 situations to use a queue and 2 situations to use
-    a stack
-  \item
-    In Python, code a \texttt{stack} class with \texttt{is\_empty},
-    \texttt{push}, and \texttt{pop} methods using the end of a Python
-    list as the top of the stack. Bonus: Compare the run time of using
-    the start of the list versus the end of the list as the top of the
-    stack using the \texttt{timeit} library!
-  \item
-    In Python, code a \texttt{binary\_search} function.
-  \item
-    In Python, code a \texttt{hash\_table} that can hash 4 values.
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}{References}
-\protect\hypertarget{references}{}
-\begin{itemize}
-\item
-  Bhargava, A. Y. (2016). \emph{Grokking algorithms: An illustrated
-  guide for programmers and other curious people.} Manning. Chapter 5.
-\item
-  Cormen, T. H. (Ed.). (2009). \emph{Introduction to algorithms} (3rd
-  ed). MIT Press. Chapter 2, 10, 11.
-\item
-  Horton, D., \& Liu, D. (2023, November 19). \emph{CSC148 Lecture
-  Notes}.
-  https://www.teach.cs.toronto.edu/\textasciitilde csc148h/winter/notes/
-\end{itemize}
-\end{frame}
-
-
-
-\end{document}
diff --git a/slides-resources/2_ds-search-sort/2_ds-search-sort.toc b/slides-resources/2_ds-search-sort/2_ds-search-sort.toc
deleted file mode 100644
index ec667ea..0000000
--- a/slides-resources/2_ds-search-sort/2_ds-search-sort.toc
+++ /dev/null
@@ -1,5 +0,0 @@
-\beamer@sectionintoc {1}{Array Based Data Structures}{3}{0}{1}
-\beamer@sectionintoc {2}{Searching}{11}{0}{2}
-\beamer@sectionintoc {3}{Sorting}{14}{0}{3}
-\beamer@sectionintoc {4}{Hash map, hash table (Python dictionary), hash functions}{18}{0}{4}
-\beamer@sectionintoc {5}{Recommended Problems and References}{24}{0}{5}
diff --git a/slides-resources/2_ds-search-sort/2_ds-search-sort.vrb b/slides-resources/2_ds-search-sort/2_ds-search-sort.vrb
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index 9bfbf86..0000000
--- a/slides-resources/2_ds-search-sort/2_ds-search-sort.vrb
+++ /dev/null
@@ -1,31 +0,0 @@
-\frametitle{Recommended Problems}
-\protect\hypertarget{recommended-problems}{}
-\begin{itemize}
-\item
-  Bhargava: Chapter 5
-
-  \begin{itemize}
-  \item
-    5.1 to 5.4
-  \item
-    Read pages 79 to 86 on the use cases of hash functions
-  \end{itemize}
-\item
-  Additional
-
-  \begin{itemize}
-  \item
-    Give examples of 2 situations to use a queue and 2 situations to use
-    a stack
-  \item
-    In Python, code a \texttt{stack} class with \texttt{is\_empty},
-    \texttt{push}, and \texttt{pop} methods using the end of a Python
-    list as the top of the stack. Bonus: Compare the run time of using
-    the start of the list versus the end of the list as the top of the
-    stack using the \texttt{timeit} library!
-  \item
-    In Python, code a \texttt{binary\_search} function.
-  \item
-    In Python, code a \texttt{hash\_table} that can hash 4 values.
-  \end{itemize}
-\end{itemize}
diff --git a/slides-resources/2_ds-search-sort/texput.log b/slides-resources/2_ds-search-sort/texput.log
deleted file mode 100644
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-
-! Emergency stop.
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-    
-End of file on the terminal!
-
- 
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-reticulate::repl_python()
-source("~/.active-rstudio-document", echo=TRUE)
-reticulate::repl_python()
-python
-python2
-python3
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-reticulate::repl_python()
-reticulate::repl_python()
-reticulate::repl_python()
-reticulate::repl_python()
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----
-title: "Recursion"
-format: 
-  beamer:
-    institute: Data Sciences Institute, UofT
-    theme: Boadilla
-    colortheme: rose
-execute:
-  echo: true
-editor: visual
-author: Salaar Liaqat
----
-
-## Outline
-
--   Call Stack
-
--   Recursion
-
--   Time and Space complexity of Recursion
-
--   Mergesort
-
--   Multiple recursive
-
-# Call Stack
-
-## How a Call Stack Works
-
--   Your computer internally uses a call stack (stack ADT) to execute functions
-
--   When you run your Python file, the `main` functions is called. `main` is pushed onto the stack
-
-    -   Sounds familiar? `if __name__ == "__main__":`
-
--   As the main function executes, it may call other functions, each functions is pushed to the top of the stack
-
-    -   The currently executing function is at the top of the stack
-
--   When each function is executed, it is popped from the stack
-
--   The function may return a value, which is passes to the calling function (the function below in the stack).
-
--   The calling function can use the return value and continue execution until the stack is empty
-
-## Basic Example
-
--   If I run `round(float("20.24"))`, I expect `20`
-
-    -   The `round` function is first to be called, it is pushed on the call stack
-
-    -   Then, `float("20.24")` is called and pushed on the call stack
-
--   Now, we pop each function off the call stack.
-
-    -   `float("20.24")` returns `20.24`
-
-    -   `round` uses the return value of the previous function, `20.24`. It executes `round(20.24)`, which returns `20`
-
-    -   The stack is empty, so the program finishes
-
-# Recursion
-
-## Motivating Example
-
--   Suppose you are looking for a key in a box, but the box contains more boxes!
-
-![](images/box-recursion.png){fig-align="center" width="7.9cm"}
-
--   2 minutes: write down the steps of the algorithm you would take to search for the key
-
-## Algorithm 1: Loop
-
-1.  Make a pile of all the boxes
-
-2.  Grab a box and open it
-
-3.  If it contains a box, append it to your pile of boxes
-
-4.  If it contains the key, you're done!
-
-5.  Repeat
-
-## Algorithm 2: Recursion
-
-1.  Grab a box and open it
-
-2.  If it contains a box, repeat step 1
-
-3.  If it contains the key, you're done!
-
-\vspace{1cm}
-
-*Which algorithm do you like more?*
-
--   Notice the function is recursive because it calls itself
-
--   Both algorithms achieve the same thing, but recursion is clearer (to me)
-
-## Formula to write a recursive function
-
--   Since recursive functions call themselves, its easy to write an infinite loop
-
--   Let's write a function that does a countdown
-
-```{python}
-def countdown(i):
-  print(i)
-  countdown(i - 1)
-```
-
--   This runs forever, so we need a *base case* to tell the code when to stop
-
-```{python}
-def countdown(i):
-  print(i)
-  if i <= 0:
-    return 
-  else:
-    countdown(i - 1)
-```
-
-## Factorial
-
--   The *factorial* is the product of all positive integers less than or equal to the given integer
-
-    -   $5! = 5 \times 4 \times 3 \times 2 \times 1 = 120$
-
-    -   We define $1! = 1$
-
--   Let's use recursion to calculate factorials
-
-```{python}
-def factorial(n):
-  if x == 1:
-    return 1
-  else:
-    return x * factorial(x - 1)
-```
-
--   Let's examine the call stack when we call `factorial(3)`
-
-## Recursion and the Call Stack
-
-![](images/rec-call-1.png){fig-align="center" width="5.9cm"}
-
-## Recursion and the Call Stack
-
-![](images/rec-call-2.png){fig-align="center" width="5.9cm"}
-
-## Recursion and the Call Stack
-
-![](images/rec-call-3.png){fig-align="center" width="5.9cm"}
-
-## Multiple Recursive Calls: Fibonacci Sequence
-
--   In calculating the factorial, each recursion only calls itself once. This doesn't have to be the case
-
--   The Fibonacci Sequence is a sequence of numbers where the first two numbers are 0 and 1, with each subsequent number being being the sum of the previous two numbers in the sequence.
-
-    -   Notice how the problem is defined recursively
-
-```{python}
-def fib(n):
-  if n <= 1:
-    return n
-  else:
-    return fib(n - 1) + fib(n - 2)
-```
-
-2 minutes: what is its time and space complexity?
-
-# Time and Space Complexity of Recursion
-
-## Time Complexity of Recursion
-
--   Generally, recursion doesn't have performance benefits compared to loops (in problems like finding a key in nested boxes)
-
-    -   However, it is simpler to understand
-
--   The time complexity of recursion depends on the number of time the function calls itself (branches)
-
-    -   Factorial: the `fact` is called $n$ times before reaching the base case so its $O(1^n) = O(n)$
-
-    -   If a recursive function called itself twice, then its $(2^n)$
-
--   When a recursive function makes multiple calls, the run time will often be $O(branches^{depth})$
-
-## Tricky Example
-
-```{python}
-#| eval: false
-def recursive(n):
-  for i in range(n):
-    # Something happens
-    i += 2
-  if n <= 0:
-    return 1
-  else:
-    return 1 + recursive(n - 3)
-```
-
--   Loop takes $n/2$ steps, because we increase `i` by 2
-
--   Recursion takes $n/3$ steps **and** the loop is called recursively.
-
-    -   In other words, for each recursion, run the loop.
-
--   The time complexity is $n/2 \times n/3 = \frac{n^2}{6} = O(n^2)$
-
-## Space complexity of recursion
-
--   Notice the call stack takes up space in memory. How much depends on the depth of the recursion
-
--   Think about the maximum amount of space the call stack will need
-
-    -   Factorial: $O(n)$, when recursion reaches the base case
-
--   Even when you have multiple branches, it's possible only 1 branch at depth $n$ is in memory at a time
-
--   2 minutes: to find the key in nested boxes, what is the memory complexity of the recursive approach versus the loop approach?
-
-## Live Coding
-
-Given an list of positive integers and an integer x, we want to find all unique combinations in the list where the sum is equal to x. A number in the list can be used multiple times.
-
-### Example
-
-```{python}
-#| eval: false
-# INPUT
-lst = [1,2,5,6]
-x = 6
-# OUTPUT
-[1, 5]
-[6]
-```
-
-# Mergesort
-
-## Divide and Conquer Algorithms
-
--   Divide and Conquer (D&C) is a general method to solve problems utilizing recursion.
-
-    -   Figure out the simplest case and use it as the base case
-
-    -   Figure out how to reduce your problem to the base case
-
--   Let's start with a trivial example: how would you sum a list of integers?
-
-    -   Solution is obvious with a loop
-
-    -   Let's do it recursively
-
-## Divide and Conquer Algorithms
-
-Step 1
-
--   What is the simplest array to sum?
-
--   Arrays with no elements or 1 element
-
-    -   sum of `[]` is 0, sum of `[8]` is 8
-
-Step 2
-
--   How can we reduce all arrays to empty array?
-
--   Notice `sum[2, 4, 5]` = 2 + `sum[4, 5]`, but the second version reduced the problem
-
-## Divide and Conquer Algorithms
-
-```{python}
-def rec_sum(lst):
-  if not lst:
-    return 0
-  else:
-    return lst[0] + rec_sum(lst[1:])
-```
-
--   Let's work on a real problem next!
-
-## Mergesort
-
-::: columns
-::: {.column width="50%"}
--   Some lists don't need to be sorted
-
-    -   Lists of size 1! This is our base case
-
--   We can split lists in half until they contain 1 element, then merge all of the sub-lists
-
--   Python's sort function uses a hybrid of merge and insertion sort, both of which you've learned!
-:::
-
-::: {.column width="50%"}
-![](images/merge-sort.png)
-:::
-:::
-
-## Big-O of Merge Sort
-
-First consider the non-recursive part of the code
-
--   The "divide" step takes linear time, since slicing operations take roughly $n/2$ steps to make a left and right copy respectively.
-
--   The merge operation also takes $n$ steps approximately
-
--   All other operations are constants
-
--   Together, the non-recursive part of this algorithm is $O(n)$
-
-Next consider the recursive calls
-
--   Recall the big-O of recursion depends on the recursion depth and number of calls. $O(branches^{depth})$
-
--   The depth in Merge Sort is the number of times you need to divide to get to a list of length 1.
-
--   Mathematically, $2^{\text{depth}} = n$, then $\text{depth} = \text{log}n$. So there are approximately log $n$ levels
-
-## Big-O of Mergesort
-
--   Since the $O(n)$ steps must be performed each recursion, the total run time is $O(n\text{log}n)$. Our analysis only depended on the size of the list, so the best and worst case of mergesort is the same
-
--   This is much faster than insertion sort!
-
--   2 minutes: does it have less space complexity than insertion sort?
-
-# Recommended Problems and References
-
-## Recommended Problems
-
--   Bhargava: Chapter 4 exercises
-
-    -   4.1 to 4.8
-
--   Write a recursive function that produces the `RecursionError: maximum recursion depth exceeded` error.
-
--   Write a iterative function to calculate the $n$th Fibonacci number. What is its time and space complexity?
-
--   Write a recursive function to determine if a string is a palindrome. What is its time and space complexity?
-
--   Write a recursive function to check if a given positive integer is a prime number. What is its time and space complexity?
-
-## Recommended Problems
-
--   Suppose you have a plot of land and want to divide the land into even square plots, while keeping the plots as big as possible. How would you do this using D&C? See Bhargava pg. 52.
-
--   Explain why the "merge" step in mergesort is $O(n)$
-
--   Implement mergesort. You might find using helper functions useful.
-
--   Write a recursive function to perform binary search on a sorted list
-
-## Bonus Readings
-
--   You may be interested in learning more about quicksort in Bhargava chapter 4 or [here](https://www.teach.cs.toronto.edu/~csc148h/winter/notes/recursive-sorting/recursive_sorting.html). Quicksort is another recursive sorting method
-
-## References
-
--   Bhargava, A. Y. (2016). *Grokking algorithms: An illustrated guide for programmers and other curious people.* Manning. Chapter 3 and 4.
-
--   Cormen, T. H. (Ed.). (2009). *Introduction to algorithms* (3rd ed). MIT Press. Chapter 4.
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-  pdftitle={Recursion},
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-\title{Recursion}
-\author{Salaar Liaqat}
-\date{}
-\institute{Data Sciences Institute, UofT}
-
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-
-\begin{frame}{Outline}
-\protect\hypertarget{outline}{}
-\begin{itemize}
-\item
-  Call Stack
-\item
-  Recursion
-\item
-  Time and Space complexity of Recursion
-\item
-  Mergesort
-\item
-  Multiple recursive
-\end{itemize}
-\end{frame}
-
-\hypertarget{call-stack}{%
-\section{Call Stack}\label{call-stack}}
-
-\begin{frame}[fragile]{How a Call Stack Works}
-\protect\hypertarget{how-a-call-stack-works}{}
-\begin{itemize}
-\item
-  Your computer internally uses a call stack (stack ADT) to execute
-  functions
-\item
-  When you run your Python file, the \texttt{main} functions is called.
-  \texttt{main} is pushed onto the stack
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Sounds familiar? \texttt{if\ \_\_name\_\_\ ==\ "\_\_main\_\_":}
-  \end{itemize}
-\item
-  As the main function executes, it may call other functions, each
-  functions is pushed to the top of the stack
-
-  \begin{itemize}
-  \tightlist
-  \item
-    The currently executing function is at the top of the stack
-  \end{itemize}
-\item
-  When each function is executed, it is popped from the stack
-\item
-  The function may return a value, which is passes to the calling
-  function (the function below in the stack).
-\item
-  The calling function can use the return value and continue execution
-  until the stack is empty
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Basic Example}
-\protect\hypertarget{basic-example}{}
-\begin{itemize}
-\item
-  If I run \texttt{round(float("20.24"))}, I expect \texttt{20}
-
-  \begin{itemize}
-  \item
-    The \texttt{round} function is first to be called, it is pushed on
-    the call stack
-  \item
-    Then, \texttt{float("20.24")} is called and pushed on the call stack
-  \end{itemize}
-\item
-  Now, we pop each function off the call stack.
-
-  \begin{itemize}
-  \item
-    \texttt{float("20.24")} returns \texttt{20.24}
-  \item
-    \texttt{round} uses the return value of the previous function,
-    \texttt{20.24}. It executes \texttt{round(20.24)}, which returns
-    \texttt{20}
-  \item
-    The stack is empty, so the program finishes
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\hypertarget{recursion}{%
-\section{Recursion}\label{recursion}}
-
-\begin{frame}{Motivating Example}
-\protect\hypertarget{motivating-example}{}
-\begin{itemize}
-\tightlist
-\item
-  Suppose you are looking for a key in a box, but the box contains more
-  boxes!
-\end{itemize}
-
-\begin{figure}
-
-{\centering \includegraphics[width=7.9cm,height=\textheight]{images/box-recursion.png}
-
-}
-
-\end{figure}
-
-\begin{itemize}
-\tightlist
-\item
-  2 minutes: write down the steps of the algorithm you would take to
-  search for the key
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Algorithm 1: Loop}
-\protect\hypertarget{algorithm-1-loop}{}
-\begin{enumerate}
-\item
-  Make a pile of all the boxes
-\item
-  Grab a box and open it
-\item
-  If it contains a box, append it to your pile of boxes
-\item
-  If it contains the key, you're done!
-\item
-  Repeat
-\end{enumerate}
-\end{frame}
-
-\begin{frame}{Algorithm 2: Recursion}
-\protect\hypertarget{algorithm-2-recursion}{}
-\begin{enumerate}
-\item
-  Grab a box and open it
-\item
-  If it contains a box, repeat step 1
-\item
-  If it contains the key, you're done!
-\end{enumerate}
-
-\vspace{1cm}
-
-\emph{Which algorithm do you like more?}
-
-\begin{itemize}
-\item
-  Notice the function is recursive because it calls itself
-\item
-  Both algorithms achieve the same thing, but recursion is clearer (to
-  me)
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Formula to write a recursive function}
-\protect\hypertarget{formula-to-write-a-recursive-function}{}
-\begin{itemize}
-\item
-  Since recursive functions call themselves, its easy to write an
-  infinite loop
-\item
-  Let's write a function that does a countdown
-\end{itemize}
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\KeywordTok{def}\NormalTok{ countdown(i):}
-  \BuiltInTok{print}\NormalTok{(i)}
-\NormalTok{  countdown(i }\OperatorTok{{-}} \DecValTok{1}\NormalTok{)}
-\end{Highlighting}
-\end{Shaded}
-
-\begin{itemize}
-\tightlist
-\item
-  This runs forever, so we need a \emph{base case} to tell the code when
-  to stop
-\end{itemize}
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\KeywordTok{def}\NormalTok{ countdown(i):}
-  \BuiltInTok{print}\NormalTok{(i)}
-  \ControlFlowTok{if}\NormalTok{ i }\OperatorTok{\textless{}=} \DecValTok{0}\NormalTok{:}
-    \ControlFlowTok{return} 
-  \ControlFlowTok{else}\NormalTok{:}
-\NormalTok{    countdown(i }\OperatorTok{{-}} \DecValTok{1}\NormalTok{)}
-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}[fragile]{Factorial}
-\protect\hypertarget{factorial}{}
-\begin{itemize}
-\item
-  The \emph{factorial} is the product of all positive integers less than
-  or equal to the given integer
-
-  \begin{itemize}
-  \item
-    \(5! = 5 \times 4 \times 3 \times 2 \times 1 = 120\)
-  \item
-    We define \(1! = 1\)
-  \end{itemize}
-\item
-  Let's use recursion to calculate factorials
-\end{itemize}
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\KeywordTok{def}\NormalTok{ factorial(n):}
-  \ControlFlowTok{if}\NormalTok{ x }\OperatorTok{==} \DecValTok{1}\NormalTok{:}
-    \ControlFlowTok{return} \DecValTok{1}
-  \ControlFlowTok{else}\NormalTok{:}
-    \ControlFlowTok{return}\NormalTok{ x }\OperatorTok{*}\NormalTok{ factorial(x }\OperatorTok{{-}} \DecValTok{1}\NormalTok{)}
-\end{Highlighting}
-\end{Shaded}
-
-\begin{itemize}
-\tightlist
-\item
-  Let's examine the call stack when we call \texttt{factorial(3)}
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Recursion and the Call Stack}
-\protect\hypertarget{recursion-and-the-call-stack}{}
-\begin{figure}
-
-{\centering \includegraphics[width=5.9cm,height=\textheight]{images/rec-call-1.png}
-
-}
-
-\end{figure}
-\end{frame}
-
-\begin{frame}{Recursion and the Call Stack}
-\protect\hypertarget{recursion-and-the-call-stack-1}{}
-\begin{figure}
-
-{\centering \includegraphics[width=5.9cm,height=\textheight]{images/rec-call-2.png}
-
-}
-
-\end{figure}
-\end{frame}
-
-\begin{frame}{Recursion and the Call Stack}
-\protect\hypertarget{recursion-and-the-call-stack-2}{}
-\begin{figure}
-
-{\centering \includegraphics[width=5.9cm,height=\textheight]{images/rec-call-3.png}
-
-}
-
-\end{figure}
-\end{frame}
-
-\begin{frame}[fragile]{Multiple Recursive Calls: Fibonacci Sequence}
-\protect\hypertarget{multiple-recursive-calls-fibonacci-sequence}{}
-\begin{itemize}
-\item
-  In calculating the factorial, each recursion only calls itself once.
-  This doesn't have to be the case
-\item
-  The Fibonacci Sequence is a sequence of numbers where the first two
-  numbers are 0 and 1, with each subsequent number being being the sum
-  of the previous two numbers in the sequence.
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Notice how the problem is defined recursively
-  \end{itemize}
-\end{itemize}
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\KeywordTok{def}\NormalTok{ fib(n):}
-  \ControlFlowTok{if}\NormalTok{ n }\OperatorTok{\textless{}=} \DecValTok{1}\NormalTok{:}
-    \ControlFlowTok{return}\NormalTok{ n}
-  \ControlFlowTok{else}\NormalTok{:}
-    \ControlFlowTok{return}\NormalTok{ fib(n }\OperatorTok{{-}} \DecValTok{1}\NormalTok{) }\OperatorTok{+}\NormalTok{ fib(n }\OperatorTok{{-}} \DecValTok{2}\NormalTok{)}
-\end{Highlighting}
-\end{Shaded}
-
-2 minutes: what is its time and space complexity?
-\end{frame}
-
-\hypertarget{time-and-space-complexity-of-recursion}{%
-\section{Time and Space Complexity of
-Recursion}\label{time-and-space-complexity-of-recursion}}
-
-\begin{frame}[fragile]{Time Complexity of Recursion}
-\protect\hypertarget{time-complexity-of-recursion}{}
-\begin{itemize}
-\item
-  Generally, recursion doesn't have performance benefits compared to
-  loops (in problems like finding a key in nested boxes)
-
-  \begin{itemize}
-  \tightlist
-  \item
-    However, it is simpler to understand
-  \end{itemize}
-\item
-  The time complexity of recursion depends on the number of time the
-  function calls itself (branches)
-
-  \begin{itemize}
-  \item
-    Factorial: the \texttt{fact} is called \(n\) times before reaching
-    the base case so its \(O(1^n) = O(n)\)
-  \item
-    If a recursive function called itself twice, then its \((2^n)\)
-  \end{itemize}
-\item
-  When a recursive function makes multiple calls, the run time will
-  often be \(O(branches^{depth})\)
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Tricky Example}
-\protect\hypertarget{tricky-example}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\KeywordTok{def}\NormalTok{ recursive(n):}
-  \ControlFlowTok{for}\NormalTok{ i }\KeywordTok{in} \BuiltInTok{range}\NormalTok{(n):}
-    \CommentTok{\# Something happens}
-\NormalTok{    i }\OperatorTok{+=} \DecValTok{2}
-  \ControlFlowTok{if}\NormalTok{ n }\OperatorTok{\textless{}=} \DecValTok{0}\NormalTok{:}
-    \ControlFlowTok{return} \DecValTok{1}
-  \ControlFlowTok{else}\NormalTok{:}
-    \ControlFlowTok{return} \DecValTok{1} \OperatorTok{+}\NormalTok{ recursive(n }\OperatorTok{{-}} \DecValTok{3}\NormalTok{)}
-\end{Highlighting}
-\end{Shaded}
-
-\begin{itemize}
-\item
-  Loop takes \(n/2\) steps, because we increase \texttt{i} by 2
-\item
-  Recursion takes \(n/3\) steps \textbf{and} the loop is called
-  recursively.
-
-  \begin{itemize}
-  \tightlist
-  \item
-    In other words, for each recursion, run the loop.
-  \end{itemize}
-\item
-  The time complexity is \(n/2 \times n/3 = \frac{n^2}{6} = O(n^2)\)
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Space complexity of recursion}
-\protect\hypertarget{space-complexity-of-recursion}{}
-\begin{itemize}
-\item
-  Notice the call stack takes up space in memory. How much depends on
-  the depth of the recursion
-\item
-  Think about the maximum amount of space the call stack will need
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Factorial: \(O(n)\), when recursion reaches the base case
-  \end{itemize}
-\item
-  Even when you have multiple branches, it's possible only 1 branch at
-  depth \(n\) is in memory at a time
-\item
-  2 minutes: to find the key in nested boxes, what is the memory
-  complexity of the recursive approach versus the loop approach?
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Live Coding}
-\protect\hypertarget{live-coding}{}
-Given an list of positive integers and an integer x, we want to find all
-unique combinations in the list where the sum is equal to x. A number in
-the list can be used multiple times.
-
-\begin{block}{Example}
-\protect\hypertarget{example}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\CommentTok{\# INPUT}
-\NormalTok{lst }\OperatorTok{=}\NormalTok{ [}\DecValTok{1}\NormalTok{,}\DecValTok{2}\NormalTok{,}\DecValTok{5}\NormalTok{,}\DecValTok{6}\NormalTok{]}
-\NormalTok{x }\OperatorTok{=} \DecValTok{6}
-\CommentTok{\# OUTPUT}
-\NormalTok{[}\DecValTok{1}\NormalTok{, }\DecValTok{5}\NormalTok{]}
-\NormalTok{[}\DecValTok{6}\NormalTok{]}
-\end{Highlighting}
-\end{Shaded}
-\end{block}
-\end{frame}
-
-\hypertarget{mergesort}{%
-\section{Mergesort}\label{mergesort}}
-
-\begin{frame}{Divide and Conquer Algorithms}
-\protect\hypertarget{divide-and-conquer-algorithms}{}
-\begin{itemize}
-\item
-  Divide and Conquer (D\&C) is a general method to solve problems
-  utilizing recursion.
-
-  \begin{itemize}
-  \item
-    Figure out the simplest case and use it as the base case
-  \item
-    Figure out how to reduce your problem to the base case
-  \end{itemize}
-\item
-  Let's start with a trivial example: how would you sum a list of
-  integers?
-
-  \begin{itemize}
-  \item
-    Solution is obvious with a loop
-  \item
-    Let's do it recursively
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Divide and Conquer Algorithms}
-\protect\hypertarget{divide-and-conquer-algorithms-1}{}
-Step 1
-
-\begin{itemize}
-\item
-  What is the simplest array to sum?
-\item
-  Arrays with no elements or 1 element
-
-  \begin{itemize}
-  \tightlist
-  \item
-    sum of \texttt{{[}{]}} is 0, sum of \texttt{{[}8{]}} is 8
-  \end{itemize}
-\end{itemize}
-
-Step 2
-
-\begin{itemize}
-\item
-  How can we reduce all arrays to empty array?
-\item
-  Notice \texttt{sum{[}2,\ 4,\ 5{]}} = 2 + \texttt{sum{[}4,\ 5{]}}, but
-  the second version reduced the problem
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Divide and Conquer Algorithms}
-\protect\hypertarget{divide-and-conquer-algorithms-2}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\KeywordTok{def}\NormalTok{ rec\_sum(lst):}
-  \ControlFlowTok{if} \KeywordTok{not}\NormalTok{ lst:}
-    \ControlFlowTok{return} \DecValTok{0}
-  \ControlFlowTok{else}\NormalTok{:}
-    \ControlFlowTok{return}\NormalTok{ lst[}\DecValTok{0}\NormalTok{] }\OperatorTok{+}\NormalTok{ rec\_sum(lst[}\DecValTok{1}\NormalTok{:])}
-\end{Highlighting}
-\end{Shaded}
-
-\begin{itemize}
-\tightlist
-\item
-  Let's work on a real problem next!
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Mergesort}
-\protect\hypertarget{mergesort-1}{}
-\begin{columns}[T]
-\begin{column}{0.5\textwidth}
-\begin{itemize}
-\item
-  Some lists don't need to be sorted
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Lists of size 1! This is our base case
-  \end{itemize}
-\item
-  We can split lists in half until they contain 1 element, then merge
-  all of the sub-lists
-\item
-  Python's sort function uses a hybrid of merge and insertion sort, both
-  of which you've learned!
-\end{itemize}
-\end{column}
-
-\begin{column}{0.5\textwidth}
-\includegraphics{images/merge-sort.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{Big-O of Merge Sort}
-\protect\hypertarget{big-o-of-merge-sort}{}
-First consider the non-recursive part of the code
-
-\begin{itemize}
-\item
-  The ``divide'' step takes linear time, since slicing operations take
-  roughly \(n/2\) steps to make a left and right copy respectively.
-\item
-  The merge operation also takes \(n\) steps approximately
-\item
-  All other operations are constants
-\item
-  Together, the non-recursive part of this algorithm is \(O(n)\)
-\end{itemize}
-
-Next consider the recursive calls
-
-\begin{itemize}
-\item
-  Recall the big-O of recursion depends on the recursion depth and
-  number of calls. \(O(branches^{depth})\)
-\item
-  The depth in Merge Sort is the number of times you need to divide to
-  get to a list of length 1.
-\item
-  Mathematically, \(2^{\text{depth}} = n\), then
-  \(\text{depth} = \text{log}n\). So there are approximately log \(n\)
-  levels
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Big-O of Mergesort}
-\protect\hypertarget{big-o-of-mergesort}{}
-\begin{itemize}
-\item
-  Since the \(O(n)\) steps must be performed each recursion, the total
-  run time is \(O(n\text{log}n)\). Our analysis only depended on the
-  size of the list, so the best and worst case of mergesort is the same
-\item
-  This is much faster than insertion sort!
-\item
-  2 minutes: does it have less space complexity than insertion sort?
-\end{itemize}
-\end{frame}
-
-\hypertarget{recommended-problems-and-references}{%
-\section{Recommended Problems and
-References}\label{recommended-problems-and-references}}
-
-\begin{frame}[fragile]{Recommended Problems}
-\protect\hypertarget{recommended-problems}{}
-\begin{itemize}
-\item
-  Bhargava: Chapter 4 exercises
-
-  \begin{itemize}
-  \tightlist
-  \item
-    4.1 to 4.8
-  \end{itemize}
-\item
-  Write a recursive function that produces the
-  \texttt{RecursionError:\ maximum\ recursion\ depth\ exceeded} error.
-\item
-  Write a iterative function to calculate the \(n\)th Fibonacci number.
-  What is its time and space complexity?
-\item
-  Write a recursive function to determine if a string is a palindrome.
-  What is its time and space complexity?
-\item
-  Write a recursive function to check if a given positive integer is a
-  prime number. What is its time and space complexity?
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Recommended Problems}
-\protect\hypertarget{recommended-problems-1}{}
-\begin{itemize}
-\item
-  Suppose you have a plot of land and want to divide the land into even
-  square plots, while keeping the plots as big as possible. How would
-  you do this using D\&C? See Bhargava pg. 52.
-\item
-  Explain why the ``merge'' step in mergesort is \(O(n)\)
-\item
-  Implement mergesort. You might find using helper functions useful.
-\item
-  Write a recursive function to perform binary search on a sorted list
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Bonus Readings}
-\protect\hypertarget{bonus-readings}{}
-\begin{itemize}
-\tightlist
-\item
-  You may be interested in learning more about quicksort in Bhargava
-  chapter 4 or
-  \href{https://www.teach.cs.toronto.edu/~csc148h/winter/notes/recursive-sorting/recursive_sorting.html}{here}.
-  Quicksort is another recursive sorting method
-\end{itemize}
-\end{frame}
-
-\begin{frame}{References}
-\protect\hypertarget{references}{}
-\begin{itemize}
-\item
-  Bhargava, A. Y. (2016). \emph{Grokking algorithms: An illustrated
-  guide for programmers and other curious people.} Manning. Chapter 3
-  and 4.
-\item
-  Cormen, T. H. (Ed.). (2009). \emph{Introduction to algorithms} (3rd
-  ed). MIT Press. Chapter 4.
-\end{itemize}
-\end{frame}
-
-
-
-\end{document}
diff --git a/slides-resources/3_recursion/3_recursion.toc b/slides-resources/3_recursion/3_recursion.toc
deleted file mode 100644
index 29289e6..0000000
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+++ /dev/null
@@ -1,5 +0,0 @@
-\beamer@sectionintoc {1}{Call Stack}{3}{0}{1}
-\beamer@sectionintoc {2}{Recursion}{6}{0}{2}
-\beamer@sectionintoc {3}{Time and Space Complexity of Recursion}{16}{0}{3}
-\beamer@sectionintoc {4}{Mergesort}{21}{0}{4}
-\beamer@sectionintoc {5}{Recommended Problems and References}{28}{0}{5}
diff --git a/slides-resources/3_recursion/3_recursion.vrb b/slides-resources/3_recursion/3_recursion.vrb
deleted file mode 100644
index b408223..0000000
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+++ /dev/null
@@ -1,24 +0,0 @@
-\frametitle{Recommended Problems}
-\protect\hypertarget{recommended-problems}{}
-\begin{itemize}
-\item
-  Bhargava: Chapter 4 exercises
-
-  \begin{itemize}
-  \tightlist
-  \item
-    4.1 to 4.8
-  \end{itemize}
-\item
-  Write a recursive function that produces the
-  \texttt{RecursionError:\ maximum\ recursion\ depth\ exceeded} error.
-\item
-  Write a iterative function to calculate the \(n\)th Fibonacci number.
-  What is its time and space complexity?
-\item
-  Write a recursive function to determine if a string is a palindrome.
-  What is its time and space complexity?
-\item
-  Write a recursive function to check if a given positive integer is a
-  prime number. What is its time and space complexity?
-\end{itemize}
diff --git a/slides-resources/4_recursive-ds/.Rhistory b/slides-resources/4_recursive-ds/.Rhistory
deleted file mode 100644
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diff --git a/slides-resources/4_recursive-ds/4_recursive-ds.pdf b/slides-resources/4_recursive-ds/4_recursive-ds.pdf
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diff --git a/slides-resources/4_recursive-ds/4_recursive-ds.qmd b/slides-resources/4_recursive-ds/4_recursive-ds.qmd
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----
-title: "Recursive Data Structures"
-format: 
-  beamer:
-    institute: Data Sciences Institute, UofT
-    theme: Boadilla
-    colortheme: rose
-execute:
-  echo: true
-editor: visual
-author: Salaar Liaqat
----
-
-## Outline
-
--   Trees
-
--   Anatomy, tree traversal methods
-
--   Binary Search Trees
-
--   Graphs
-
--   Nearest Neighbor Problem
-
-# Trees
-
-## Introduction to Trees
-
--   Not all data has a natural linear order. Organization charts and file storage systems have a *hierarchical structure*, in which each entity is linked to multiple entities below it
-
--   This type of data is represented using a *tree*. A tree is either
-
-    -   Empty
-
-    -   Has a *root value* connected to any number of other trees, called *subtrees*
-
--   We draw the root at the top of the tree
-
-## Anatomy of a Tree
-
-::: columns
-::: {.column width="60%"}
--   The *size* of a tree is the number of values in the tree
-
--   A *leaf* is a value with no subtrees. The leaves of this tree are labeled E, F, G, J, and I
-
--   The *height* of a tree is the longest path from its root to its leaves. The height of this tree is 4
-:::
-
-::: {.column width="40%"}
-![](images/tree.png)
-:::
-:::
-
-## Anatomy of a Tree
-
-::: columns
-::: {.column width="60%"}
--   The *children* of a value are all values directly connected underneath that value. The children of A are B, C, and D
-
--   The *descendants* of a value are it's children, the children of its children, etc. This can be defined recursively
-
--   The *parent* of a value is the value immediately above and connected to it. The parent of H is C
-
--   The *ancestors* of a value are its parent, the parent of its parent, etc.
-:::
-
-::: {.column width="40%"}
-![](images/tree.png)
-:::
-:::
-
-## Tree Traversal Methods
-
--   Linear data structures only have one logical way to traverse them. Trees can be traversed in different ways
-
--   We'll look at the following methods of tree traversal and their applications
-
-    -   *Depth First Search* (DFS): Inorder, Preorder, and Postorder traversal
-
-    -   *Breadth First Search* (BFS)
-
--   Note there are other methods not covered
-
-## DFS: Inorder Traversal
-
-::: columns
-::: {.column width="60%"}
-1.  Traverse the left subtree
-
-2.  Visit the root
-
-3.  Traverse the right subtree
-
-\vspace{1cm}
-
-Result: 4 2 5 1 6 3
-:::
-
-::: {.column width="40%"}
-![](images/tree-num.png)
-:::
-:::
-
-## DFS: Inorder Traversal Code
-
-Let's look at the code to do this
-
-```{python}
-class Node:
-  """Tree class
-  """
-  def __init__(self, key):
-    self.left = None
-    self.right = None
-    self.val = key
-    
-def print_inorder(root):
-  if root:
-    print_inorder(root.left)
-    print(root.val, end = " ")
-    print_inorder(root.right)
-```
-
-## DFS: Inorder Traversal Code
-
-```{python}
-root = Node(1)
-root.left = Node(2)
-root.right = Node(3)
-root.left.left = Node(4)
-root.left.right = Node(5)
-root.right.left = Node(6)
-print_inorder(root)
-```
-
-In binary search trees (next section), inorder traversal gives the nodes in a non-decreasing order.
-
-## DFS: Inorder Traversal Complexity
-
-Time complexity
-
--   Each node is visited exactly once. The work done at each node is constant. $O(n)$
-
-Space complexity
-
--   Dependent on the maximum depth of the recursion, which is the height of the tree. $O(h)$
-
-## DFS: Preorder Traversal
-
-::: columns
-::: {.column width="60%"}
-1.  Visit the root
-
-2.  Traverse the left subtree
-
-3.  Traverse the right subtree
-
-\vspace{1cm}
-
-Result: 1 2 4 5 3 6
-
-Preorder traversal is used to create a copy of the tree
-:::
-
-::: {.column width="40%"}
-![](images/tree-num.png)
-:::
-:::
-
-## DFS: Postorder Traversal
-
-::: columns
-::: {.column width="60%"}
-1.  Traverse the left subtree
-
-2.  Traverse the right subtree
-
-3.  Visit the root
-
-\vspace{1cm}
-
-Result: 4 5 2 6 3 1
-
-Preorder traversal is used to delete subtrees. (why?)
-:::
-
-::: {.column width="40%"}
-![](images/tree-num.png)
-:::
-:::
-
-## BFS
-
-::: columns
-::: {.column width="60%"}
-BFS (or Level Order Traversal) traverses nodes present in the same level before traversing the next level
-
-1.  For each node
-
--   The node is visited
-
--   The child nodes are enqueued in a FIFO queue
-
-2.  First node is dequeued
-
-3.  Child nodes are enqueued
-
-4.  Repeat until the queue is empty
-
-Result: 1 2 3 4 5 6
-:::
-
-::: {.column width="40%"}
-![](images/tree-num.png)
-:::
-:::
-
-# Binary Search Trees
-
-## BST Definitions
-
--   You can think of a BST as a sorted tree
-
--   A *binary tree* is a tree in which every item has at most two subtrees
-
-    -   The tree used in illustrating DFS and BFS methods is a binary tree
-
--   A binary tree is a *binary search tree property* if its value is greater than or equal to all items in the left subtree
-
--   A binary tree is a *binary search tree* if every item in the tree satisfies the binary search tree property
-
-## BST Efficiency
-
-::: columns
-::: {.column width="70%"}
--   Consider the BST on the right. Verify that it is a BST.
-
--   The worst-case run time is $O(h)$, $h$ being the height of the tree
-
-    -   So the tree on the right is $O(n)$
-
--   A tree of height $h$ can have at most $2^h - 1$ nodes. So we need at least log$n$ height to store all of them.
-
-    -   So if the tree was balanced, then it would be $O(\text{log}n)$
-:::
-
-::: {.column width="30%"}
-![](images/tree-unbal.png)
-:::
-:::
-
-## BST Efficiency
-
--   Convince yourself that for a balanced BST the search, insert, and delete Big-O is all $O(\text{log}n)$
-
--   Ensuring that a tree is balanced is important
-
-    -   Red-Black trees (not covered) are trees that balance themselves
-
-    -   You may also be interested in B-trees, which are used in databases
-
-## Live Coding
-
-Given a BST, insert a new node in this BST.
-
-![](images/insertion.png){width="461"}
-
-# Graphs
-
-## Introduction
-
--   We looked at lists and trees, which represent linear and hierarchical relationships respectively
-
-    -   But many relationships are neither
-
-    -   Friend networks, internet connections, flight connections
-
--   Graphs consist of two parts, *nodes* and *edges*
-
-    -   A node connected to another is a *neighbor* \vspace{1cm}
-
-![](images/graph-anat.png){fig-align="center" width="156"}
-
-## Types of Graphs
-
-::: columns
-::: {.column width="60%"}
-There are directed and undirected graphs to represent different situations
-
--   Friendships: undirected
-
--   Twitter followers: directed
-
--   Who owes who money: directed
-
--   Note that trees are special cases of directed graphs
-
-Graphs can also be weighted, to differentiate strengths between nodes
-
-There are two questions we ask about graphs: Is there a path from node A to B? What is the shortest path from node A to B? BFS answers both!
-:::
-
-::: {.column width="40%"}
-![](images/graph-weight.png)
-:::
-:::
-
-## BFS of Graphs
-
--   *Breadth First Search* (BFS) searches graph for a node that meets a set of criteria. It starts at the root of the graph and visits all nodes at the current depth level before moving on to the nodes at the next depth level
-
-    -   If there are multiple nodes meeting the criteria, then BFS will also find the nearest node!
-
--   The issue is that graphs contain *cycles*, so we may visit the same node more than once
-
-    -   Let's split edges into visited and not visited
-
--   We use a list to keep track of visited nodes
-
--   All the adjacent unvisited nodes of the current level are pushed into the queue and the nodes of the current level are marked visited and popped from the queue
-
--   Is BFS a recursive or iterative graph search method?
-
-## BFS Example
-
-::: columns
-::: {.column width="60%"}
--   Let's traverse a graph with BFS starting at node "1"
-
--   Visited list and queue start as empty \vspace{1cm}
-
-Visited: \[ , , , , \]
-
-Queue: \[ , , , , \]
-:::
-
-::: {.column width="40%"}
-![](images/graph-bfs.png)
-:::
-:::
-
-## BFS Example
-
-::: columns
-::: {.column width="60%"}
--   We're at node 1, so we push it onto the visited list and push it onto the queue \vspace{1cm}
-
-Visited: \[1, , , , \]
-
-Queue: \[1, , , , \]
-:::
-
-::: {.column width="40%"}
-![](images/graph-bfs.png)
-:::
-:::
-
-## BFS Example
-
-::: columns
-::: {.column width="60%"}
--   Now we visited 1, so it is dequeued.
-
--   At the first level away from node 1, there is 3 and 6.
-
--   We visit 3 and 6, but we have not visited any of it's neighbors (other than 1), so 3 and 6 are enqueued. \vspace{1cm}
-
-Visited: \[1, 3, 6, , \]
-
-Queue: \[3, 6, , , \]
-:::
-
-::: {.column width="40%"}
-![](images/graph-bfs.png)
-:::
-:::
-
-## BFS Example
-
-::: columns
-::: {.column width="60%"}
--   Visit the neighbors of node 3, so we dequeue it
-
--   But we need to enqueue 10, because we haven't visited its neighbors \vspace{1cm}
-
-Visited: \[1, 3, 6, 10, \]
-
-Queue: \[6, 10, , \]
-:::
-
-::: {.column width="40%"}
-![](images/graph-bfs.png)
-:::
-:::
-
-## BFS Example
-
-::: columns
-::: {.column width="60%"}
--   Visit the neighbors of node 6, which is just 7, so we dequeue it
-
--   But we need to enqueue 7 \vspace{1cm}
-
-Visited: \[1, 3, 6, 10, 7\]
-
-Queue: \[10, 7, , \]
-:::
-
-::: {.column width="40%"}
-![](images/graph-bfs.png)
-:::
-:::
-
-## BFS Example
-
-::: columns
-::: {.column width="60%"}
--   Visit the neighbors of node 10, and dequeue 10
-
--   But we already visited those nodes, so the visited list does not change \vspace{1cm}
-
-Visited: \[1, 3, 6, 10, 7\]
-
-Queue: \[7, , , , \]
-:::
-
-::: {.column width="40%"}
-![](images/graph-bfs.png)
-:::
-:::
-
-## BFS Example
-
-::: columns
-::: {.column width="60%"}
--   Visit neighbors of 7, which are also all visited
-
--   The queue is empty, so the algorithm ends \vspace{1cm}
-
-Visited: \[1, 3, 6, 10, 7\]
-
-Queue: \[ , , , , \]
-:::
-
-::: {.column width="40%"}
-![](images/graph-bfs.png)
-:::
-:::
-
-## Time and Space Complexity of BFS
-
--   Each edge and each node must be visited once, so the time complexity is $O(n + e)$
-
--   Since we need to store each node of the graph by the end of the algorithm, the space complexity is $O(n)$
-
-## Implementing Graphs and BFS
-
-We can represent graphs using the *adjacency list* representation
-
--   Other options include adjacency matrix or using a Python library
-
-```{python}
-from collections import deque
-
-class Graph:
-  def __init__(self):
-    self.graph = {}
-
-  def add_edge(self, vertex, neighbors):
-    self.graph[vertex] = neighbors
-```
-
-## Implementing Graphs and BFS
-
-```{python}
-def bfs(graph, start):
-  visited = set()
-  queue = deque([start])
-
-  while queue:
-    current_vertex = queue.popleft()
-
-    if current_vertex not in visited:
-      # Process the current vertex
-      print(current_vertex, end=' ')
-      visited.add(current_vertex)
-
-      # Enqueue unvisited neighbors
-      for neighbor in graph.graph.get(current_vertex, []):
-        if neighbor not in visited:
-          queue.append(neighbor)
-```
-
-## Implementing Graphs and BFS
-
-```{python}
-# Represent graph from above
-ex_graph = Graph()
-ex_graph.add_edge(1, [3, 6])
-ex_graph.add_edge(3, [10, 6])
-ex_graph.add_edge(6, [3, 7])
-ex_graph.add_edge(10, [3, 7])
-ex_graph.add_edge(7, [10, 6])
-
-# Perform BFS starting from vertex 1
-bfs(ex_graph, 1)
-```
-
-## Recursive Graph Search: Preorder Traversal
-
--   Using the same `Graph` class, let's implement preorder traversal
-
-```{python}
-def recursive_preorder_traversal(graph, start, visited=None):
-  if visited is None:
-    visited = set()
-
-  # Process the current vertex
-  print(start, end=' ')
-  visited.add(start)
-
-    # Recursive traversal of neighbors
-  for neighbor in graph.graph.get(start, []):
-    if neighbor not in visited:
-      recursive_preorder_traversal(graph, neighbor, visited)
-```
-
-## Recursive Graph Search: Preorder Traversal
-
-```{python}
-bfs(ex_graph, 1)
-```
-
-# Nearest Neighour Problem
-
-## Nearest Neighbour Problem
-
--   As you may have encountered already, machine learning and statistical methods often depend on finding the nearest neighbor to a data point
-
-    -   K-nearest neighbors regression, propensity score matching
-
--   In a $k$ dimensional space, if we conduct a linear search for points, the running time will be $O(kn)$ for $n$ data points.
-
--   Can we do better?
-
-## k-d Trees
-
--   k-d trees is short for k dimensional tree (notation is a bit unfortunate, different K than KNN)
-
-    -   It is useful for multidimensional searches
-
--   Let's discuss the properties of k-d trees and why they work
-
--   Binary tree where each node represents an axis-aligned hyperrectangle in the k-dimensional space
-
-    -   hyperrectangle: rectangle in higher dimensions
-
--   Nodes in the left subtree have coordinates less than the splitting dimension of the current node, while nodes in the right subtree have coordinates greater than the splitting dimension.
-
--   At each level of the tree, a specific dimension is chosen to split the data. The choice of dimension alternates as we traverse down the tree.
-
--   Each leaf represents a single point in the k-dimensional space
-
-## k-d Trees Animation
-
-<https://commons.wikimedia.org/wiki/File:KDTree-animation.gif>
-
-## Applications and Issues
-
--   Notice k-d trees can also find values within a certain range very quickly, not just a specific point
-
--   GIS (geographic information systems) queries
-
--   KNN algorithm
-
--   Computer graphics, such as ray tracing to facilitate efficient space partitioning
-
--   Issues occur in high-dimensional spaces and trees can become imbalanced
-
-# Recommended Problems and References
-
-## Recommended Readings
-
--   Bhargava: Chapter 6
-
--   Bhargava: Chapter 11, pages 203 to 206 about Trees
-
-## Recommended Problems
-
--   Cormen: Chapter 10 exercises
-
-    -   10.3-1, 10.3-2, 10.3-3
-
--   Bhargava: Chapter 6 exercises
-
-    -   6.1 to 6.5
-
-## Recommended Problems
-
--   Implement preorder, postorder, and level order traversal. Determine the time and space complexity in each case
-
--   Implement a function that find an element in a BST and deletes it. The descendants of the deleted node are given to the deleted node's parent.
-
--   Using the `graph` class from the slides, implement BST search such that it stops and tell you the distance the node is from the starting point.
-
-    -   For instance, if we searched for 7 in the graph given in the slides, it would return `"Found! Distance 2"`.
-
-    -   If we searched for 100 in the graph, it would return `"Not found!"`
-
--   Implement postorder graph traversal using the `graph` class from the slides.
-
--   Implement a function using recursion to find the sum heterogeneous nested lists such as \[\[1, \[2\]\], \[\[\[3\]\]\], 4, \[\[5, 6\], \[\[\[7\]\]\]\]\].
-
-## References
-
--   Bhargava, A. Y. (2016). *Grokking algorithms: An illustrated guide for programmers and other curious people.* Manning. Chapter 6, 10, 11.
-
--   Cormen, T. H. (Ed.). (2009). *Introduction to algorithms* (3rd ed). MIT Press. Chapter 12 and 20.
-
--   Horton, D., & Liu, D. (2023, November 19). *CSC148 Lecture Notes*. https://www.teach.cs.toronto.edu/\~csc148h/winter/notes/
diff --git a/slides-resources/4_recursive-ds/4_recursive-ds.snm b/slides-resources/4_recursive-ds/4_recursive-ds.snm
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diff --git a/slides-resources/4_recursive-ds/4_recursive-ds.tex b/slides-resources/4_recursive-ds/4_recursive-ds.tex
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-  pdftitle={Recursive Data Structures},
-  pdfauthor={Salaar Liaqat},
-  hidelinks,
-  pdfcreator={LaTeX via pandoc}}
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-\title{Recursive Data Structures}
-\author{Salaar Liaqat}
-\date{}
-\institute{Data Sciences Institute, UofT}
-
-\begin{document}
-\frame{\titlepage}
-\ifdefined\Shaded\renewenvironment{Shaded}{\begin{tcolorbox}[borderline west={3pt}{0pt}{shadecolor}, boxrule=0pt, frame hidden, interior hidden, sharp corners, breakable, enhanced]}{\end{tcolorbox}}\fi
-
-\begin{frame}{Outline}
-\protect\hypertarget{outline}{}
-\begin{itemize}
-\item
-  Trees
-\item
-  Anatomy, tree traversal methods
-\item
-  Binary Search Trees
-\item
-  Graphs
-\item
-  Nearest Neighbor Problem
-\end{itemize}
-\end{frame}
-
-\hypertarget{trees}{%
-\section{Trees}\label{trees}}
-
-\begin{frame}{Introduction to Trees}
-\protect\hypertarget{introduction-to-trees}{}
-\begin{itemize}
-\item
-  Not all data has a natural linear order. Organization charts and file
-  storage systems have a \emph{hierarchical structure}, in which each
-  entity is linked to multiple entities below it
-\item
-  This type of data is represented using a \emph{tree}. A tree is either
-
-  \begin{itemize}
-  \item
-    Empty
-  \item
-    Has a \emph{root value} connected to any number of other trees,
-    called \emph{subtrees}
-  \end{itemize}
-\item
-  We draw the root at the top of the tree
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Anatomy of a Tree}
-\protect\hypertarget{anatomy-of-a-tree}{}
-\begin{columns}[T]
-\begin{column}{0.6\textwidth}
-\begin{itemize}
-\item
-  The \emph{size} of a tree is the number of values in the tree
-\item
-  A \emph{leaf} is a value with no subtrees. The leaves of this tree are
-  labeled E, F, G, J, and I
-\item
-  The \emph{height} of a tree is the longest path from its root to its
-  leaves. The height of this tree is 4
-\end{itemize}
-\end{column}
-
-\begin{column}{0.4\textwidth}
-\includegraphics{images/tree.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{Anatomy of a Tree}
-\protect\hypertarget{anatomy-of-a-tree-1}{}
-\begin{columns}[T]
-\begin{column}{0.6\textwidth}
-\begin{itemize}
-\item
-  The \emph{children} of a value are all values directly connected
-  underneath that value. The children of A are B, C, and D
-\item
-  The \emph{descendants} of a value are it's children, the children of
-  its children, etc. This can be defined recursively
-\item
-  The \emph{parent} of a value is the value immediately above and
-  connected to it. The parent of H is C
-\item
-  The \emph{ancestors} of a value are its parent, the parent of its
-  parent, etc.
-\end{itemize}
-\end{column}
-
-\begin{column}{0.4\textwidth}
-\includegraphics{images/tree.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{Tree Traversal Methods}
-\protect\hypertarget{tree-traversal-methods}{}
-\begin{itemize}
-\item
-  Linear data structures only have one logical way to traverse them.
-  Trees can be traversed in different ways
-\item
-  We'll look at the following methods of tree traversal and their
-  applications
-
-  \begin{itemize}
-  \item
-    \emph{Depth First Search} (DFS): Inorder, Preorder, and Postorder
-    traversal
-  \item
-    \emph{Breadth First Search} (BFS)
-  \end{itemize}
-\item
-  Note there are other methods not covered
-\end{itemize}
-\end{frame}
-
-\begin{frame}{DFS: Inorder Traversal}
-\protect\hypertarget{dfs-inorder-traversal}{}
-\begin{columns}[T]
-\begin{column}{0.6\textwidth}
-\begin{enumerate}
-\item
-  Traverse the left subtree
-\item
-  Visit the root
-\item
-  Traverse the right subtree
-\end{enumerate}
-
-\vspace{1cm}
-
-Result: 4 2 5 1 6 3
-\end{column}
-
-\begin{column}{0.4\textwidth}
-\includegraphics{images/tree-num.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}[fragile]{DFS: Inorder Traversal Code}
-\protect\hypertarget{dfs-inorder-traversal-code}{}
-Let's look at the code to do this
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\KeywordTok{class}\NormalTok{ Node:}
-  \CommentTok{"""Tree class}
-\CommentTok{  """}
-  \KeywordTok{def} \FunctionTok{\_\_init\_\_}\NormalTok{(}\VariableTok{self}\NormalTok{, key):}
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-    \VariableTok{self}\NormalTok{.right }\OperatorTok{=} \VariableTok{None}
-    \VariableTok{self}\NormalTok{.val }\OperatorTok{=}\NormalTok{ key}
-    
-\KeywordTok{def}\NormalTok{ print\_inorder(root):}
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-\NormalTok{    print\_inorder(root.left)}
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-\NormalTok{    print\_inorder(root.right)}
-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}[fragile]{DFS: Inorder Traversal Code}
-\protect\hypertarget{dfs-inorder-traversal-code-1}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\NormalTok{root }\OperatorTok{=}\NormalTok{ Node(}\DecValTok{1}\NormalTok{)}
-\NormalTok{root.left }\OperatorTok{=}\NormalTok{ Node(}\DecValTok{2}\NormalTok{)}
-\NormalTok{root.right }\OperatorTok{=}\NormalTok{ Node(}\DecValTok{3}\NormalTok{)}
-\NormalTok{root.left.left }\OperatorTok{=}\NormalTok{ Node(}\DecValTok{4}\NormalTok{)}
-\NormalTok{root.left.right }\OperatorTok{=}\NormalTok{ Node(}\DecValTok{5}\NormalTok{)}
-\NormalTok{root.right.left }\OperatorTok{=}\NormalTok{ Node(}\DecValTok{6}\NormalTok{)}
-\NormalTok{print\_inorder(root)}
-\end{Highlighting}
-\end{Shaded}
-
-\begin{verbatim}
-4 2 5 1 6 3 
-\end{verbatim}
-
-In binary search trees (next section), inorder traversal gives the nodes
-in a non-decreasing order.
-\end{frame}
-
-\begin{frame}{DFS: Inorder Traversal Complexity}
-\protect\hypertarget{dfs-inorder-traversal-complexity}{}
-Time complexity
-
-\begin{itemize}
-\tightlist
-\item
-  Each node is visited exactly once. The work done at each node is
-  constant. \(O(n)\)
-\end{itemize}
-
-Space complexity
-
-\begin{itemize}
-\tightlist
-\item
-  Dependent on the maximum depth of the recursion, which is the height
-  of the tree. \(O(h)\)
-\end{itemize}
-\end{frame}
-
-\begin{frame}{DFS: Preorder Traversal}
-\protect\hypertarget{dfs-preorder-traversal}{}
-\begin{columns}[T]
-\begin{column}{0.6\textwidth}
-\begin{enumerate}
-\item
-  Visit the root
-\item
-  Traverse the left subtree
-\item
-  Traverse the right subtree
-\end{enumerate}
-
-\vspace{1cm}
-
-Result: 1 2 4 5 3 6
-
-Preorder traversal is used to create a copy of the tree
-\end{column}
-
-\begin{column}{0.4\textwidth}
-\includegraphics{images/tree-num.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{DFS: Postorder Traversal}
-\protect\hypertarget{dfs-postorder-traversal}{}
-\begin{columns}[T]
-\begin{column}{0.6\textwidth}
-\begin{enumerate}
-\item
-  Traverse the left subtree
-\item
-  Traverse the right subtree
-\item
-  Visit the root
-\end{enumerate}
-
-\vspace{1cm}
-
-Result: 4 5 2 6 3 1
-
-Preorder traversal is used to delete subtrees. (why?)
-\end{column}
-
-\begin{column}{0.4\textwidth}
-\includegraphics{images/tree-num.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{BFS}
-\protect\hypertarget{bfs}{}
-\begin{columns}[T]
-\begin{column}{0.6\textwidth}
-BFS (or Level Order Traversal) traverses nodes present in the same level
-before traversing the next level
-
-\begin{enumerate}
-\tightlist
-\item
-  For each node
-\end{enumerate}
-
-\begin{itemize}
-\item
-  The node is visited
-\item
-  The child nodes are enqueued in a FIFO queue
-\end{itemize}
-
-\begin{enumerate}
-\setcounter{enumi}{1}
-\item
-  First node is dequeued
-\item
-  Child nodes are enqueued
-\item
-  Repeat until the queue is empty
-\end{enumerate}
-
-Result: 1 2 3 4 5 6
-\end{column}
-
-\begin{column}{0.4\textwidth}
-\includegraphics{images/tree-num.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\hypertarget{binary-search-trees}{%
-\section{Binary Search Trees}\label{binary-search-trees}}
-
-\begin{frame}{BST Definitions}
-\protect\hypertarget{bst-definitions}{}
-\begin{itemize}
-\item
-  You can think of a BST as a sorted tree
-\item
-  A \emph{binary tree} is a tree in which every item has at most two
-  subtrees
-
-  \begin{itemize}
-  \tightlist
-  \item
-    The tree used in illustrating DFS and BFS methods is a binary tree
-  \end{itemize}
-\item
-  A binary tree is a \emph{binary search tree property} if its value is
-  greater than or equal to all items in the left subtree
-\item
-  A binary tree is a \emph{binary search tree} if every item in the tree
-  satisfies the binary search tree property
-\end{itemize}
-\end{frame}
-
-\begin{frame}{BST Efficiency}
-\protect\hypertarget{bst-efficiency}{}
-\begin{columns}[T]
-\begin{column}{0.7\textwidth}
-\begin{itemize}
-\item
-  Consider the BST on the right. Verify that it is a BST.
-\item
-  The worst-case run time is \(O(h)\), \(h\) being the height of the
-  tree
-
-  \begin{itemize}
-  \tightlist
-  \item
-    So the tree on the right is \(O(n)\)
-  \end{itemize}
-\item
-  A tree of height \(h\) can have at most \(2^h - 1\) nodes. So we need
-  at least log\(n\) height to store all of them.
-
-  \begin{itemize}
-  \tightlist
-  \item
-    So if the tree was balanced, then it would be \(O(\text{log}n)\)
-  \end{itemize}
-\end{itemize}
-\end{column}
-
-\begin{column}{0.3\textwidth}
-\includegraphics{images/tree-unbal.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{BST Efficiency}
-\protect\hypertarget{bst-efficiency-1}{}
-\begin{itemize}
-\item
-  Convince yourself that for a balanced BST the search, insert, and
-  delete Big-O is all \(O(\text{log}n)\)
-\item
-  Ensuring that a tree is balanced is important
-
-  \begin{itemize}
-  \item
-    Red-Black trees (not covered) are trees that balance themselves
-  \item
-    You may also be interested in B-trees, which are used in databases
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Live Coding}
-\protect\hypertarget{live-coding}{}
-Given a BST, insert a new node in this BST.
-
-\includegraphics[width=4.80208in,height=\textheight]{images/insertion.png}
-\end{frame}
-
-\hypertarget{graphs}{%
-\section{Graphs}\label{graphs}}
-
-\begin{frame}{Introduction}
-\protect\hypertarget{introduction}{}
-\begin{itemize}
-\item
-  We looked at lists and trees, which represent linear and hierarchical
-  relationships respectively
-
-  \begin{itemize}
-  \item
-    But many relationships are neither
-  \item
-    Friend networks, internet connections, flight connections
-  \end{itemize}
-\item
-  Graphs consist of two parts, \emph{nodes} and \emph{edges}
-
-  \begin{itemize}
-  \tightlist
-  \item
-    A node connected to another is a \emph{neighbor} \vspace{1cm}
-  \end{itemize}
-\end{itemize}
-
-\begin{figure}
-
-{\centering \includegraphics[width=1.625in,height=\textheight]{images/graph-anat.png}
-
-}
-
-\end{figure}
-\end{frame}
-
-\begin{frame}{Types of Graphs}
-\protect\hypertarget{types-of-graphs}{}
-\begin{columns}[T]
-\begin{column}{0.6\textwidth}
-There are directed and undirected graphs to represent different
-situations
-
-\begin{itemize}
-\item
-  Friendships: undirected
-\item
-  Twitter followers: directed
-\item
-  Who owes who money: directed
-\item
-  Note that trees are special cases of directed graphs
-\end{itemize}
-
-Graphs can also be weighted, to differentiate strengths between nodes
-
-There are two questions we ask about graphs: Is there a path from node A
-to B? What is the shortest path from node A to B? BFS answers both!
-\end{column}
-
-\begin{column}{0.4\textwidth}
-\includegraphics{images/graph-weight.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{BFS of Graphs}
-\protect\hypertarget{bfs-of-graphs}{}
-\begin{itemize}
-\item
-  \emph{Breadth First Search} (BFS) searches graph for a node that meets
-  a set of criteria. It starts at the root of the graph and visits all
-  nodes at the current depth level before moving on to the nodes at the
-  next depth level
-
-  \begin{itemize}
-  \tightlist
-  \item
-    If there are multiple nodes meeting the criteria, then BFS will also
-    find the nearest node!
-  \end{itemize}
-\item
-  The issue is that graphs contain \emph{cycles}, so we may visit the
-  same node more than once
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Let's split edges into visited and not visited
-  \end{itemize}
-\item
-  We use a list to keep track of visited nodes
-\item
-  All the adjacent unvisited nodes of the current level are pushed into
-  the queue and the nodes of the current level are marked visited and
-  popped from the queue
-\item
-  Is BFS a recursive or iterative graph search method?
-\end{itemize}
-\end{frame}
-
-\begin{frame}{BFS Example}
-\protect\hypertarget{bfs-example}{}
-\begin{columns}[T]
-\begin{column}{0.6\textwidth}
-\begin{itemize}
-\item
-  Let's traverse a graph with BFS starting at node ``1''
-\item
-  Visited list and queue start as empty \vspace{1cm}
-\end{itemize}
-
-Visited: {[} , , , , {]}
-
-Queue: {[} , , , , {]}
-\end{column}
-
-\begin{column}{0.4\textwidth}
-\includegraphics{images/graph-bfs.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{BFS Example}
-\protect\hypertarget{bfs-example-1}{}
-\begin{columns}[T]
-\begin{column}{0.6\textwidth}
-\begin{itemize}
-\tightlist
-\item
-  We're at node 1, so we push it onto the visited list and push it onto
-  the queue \vspace{1cm}
-\end{itemize}
-
-Visited: {[}1, , , , {]}
-
-Queue: {[}1, , , , {]}
-\end{column}
-
-\begin{column}{0.4\textwidth}
-\includegraphics{images/graph-bfs.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{BFS Example}
-\protect\hypertarget{bfs-example-2}{}
-\begin{columns}[T]
-\begin{column}{0.6\textwidth}
-\begin{itemize}
-\item
-  Now we visited 1, so it is dequeued.
-\item
-  At the first level away from node 1, there is 3 and 6.
-\item
-  We visit 3 and 6, but we have not visited any of it's neighbors (other
-  than 1), so 3 and 6 are enqueued. \vspace{1cm}
-\end{itemize}
-
-Visited: {[}1, 3, 6, , {]}
-
-Queue: {[}3, 6, , , {]}
-\end{column}
-
-\begin{column}{0.4\textwidth}
-\includegraphics{images/graph-bfs.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{BFS Example}
-\protect\hypertarget{bfs-example-3}{}
-\begin{columns}[T]
-\begin{column}{0.6\textwidth}
-\begin{itemize}
-\item
-  Visit the neighbors of node 3, so we dequeue it
-\item
-  But we need to enqueue 10, because we haven't visited its neighbors
-  \vspace{1cm}
-\end{itemize}
-
-Visited: {[}1, 3, 6, 10, {]}
-
-Queue: {[}6, 10, , {]}
-\end{column}
-
-\begin{column}{0.4\textwidth}
-\includegraphics{images/graph-bfs.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{BFS Example}
-\protect\hypertarget{bfs-example-4}{}
-\begin{columns}[T]
-\begin{column}{0.6\textwidth}
-\begin{itemize}
-\item
-  Visit the neighbors of node 6, which is just 7, so we dequeue it
-\item
-  But we need to enqueue 7 \vspace{1cm}
-\end{itemize}
-
-Visited: {[}1, 3, 6, 10, 7{]}
-
-Queue: {[}10, 7, , {]}
-\end{column}
-
-\begin{column}{0.4\textwidth}
-\includegraphics{images/graph-bfs.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{BFS Example}
-\protect\hypertarget{bfs-example-5}{}
-\begin{columns}[T]
-\begin{column}{0.6\textwidth}
-\begin{itemize}
-\item
-  Visit the neighbors of node 10, and dequeue 10
-\item
-  But we already visited those nodes, so the visited list does not
-  change \vspace{1cm}
-\end{itemize}
-
-Visited: {[}1, 3, 6, 10, 7{]}
-
-Queue: {[}7, , , , {]}
-\end{column}
-
-\begin{column}{0.4\textwidth}
-\includegraphics{images/graph-bfs.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{BFS Example}
-\protect\hypertarget{bfs-example-6}{}
-\begin{columns}[T]
-\begin{column}{0.6\textwidth}
-\begin{itemize}
-\item
-  Visit neighbors of 7, which are also all visited
-\item
-  The queue is empty, so the algorithm ends \vspace{1cm}
-\end{itemize}
-
-Visited: {[}1, 3, 6, 10, 7{]}
-
-Queue: {[} , , , , {]}
-\end{column}
-
-\begin{column}{0.4\textwidth}
-\includegraphics{images/graph-bfs.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{Time and Space Complexity of BFS}
-\protect\hypertarget{time-and-space-complexity-of-bfs}{}
-\begin{itemize}
-\item
-  Each edge and each node must be visited once, so the time complexity
-  is \(O(n + e)\)
-\item
-  Since we need to store each node of the graph by the end of the
-  algorithm, the space complexity is \(O(n)\)
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Implementing Graphs and BFS}
-\protect\hypertarget{implementing-graphs-and-bfs}{}
-We can represent graphs using the \emph{adjacency list} representation
-
-\begin{itemize}
-\tightlist
-\item
-  Other options include adjacency matrix or using a Python library
-\end{itemize}
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\ImportTok{from}\NormalTok{ collections }\ImportTok{import}\NormalTok{ deque}
-
-\KeywordTok{class}\NormalTok{ Graph:}
-  \KeywordTok{def} \FunctionTok{\_\_init\_\_}\NormalTok{(}\VariableTok{self}\NormalTok{):}
-    \VariableTok{self}\NormalTok{.graph }\OperatorTok{=}\NormalTok{ \{\}}
-
-  \KeywordTok{def}\NormalTok{ add\_edge(}\VariableTok{self}\NormalTok{, vertex, neighbors):}
-    \VariableTok{self}\NormalTok{.graph[vertex] }\OperatorTok{=}\NormalTok{ neighbors}
-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}[fragile]{Implementing Graphs and BFS}
-\protect\hypertarget{implementing-graphs-and-bfs-1}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\KeywordTok{def}\NormalTok{ bfs(graph, start):}
-\NormalTok{  visited }\OperatorTok{=} \BuiltInTok{set}\NormalTok{()}
-\NormalTok{  queue }\OperatorTok{=}\NormalTok{ deque([start])}
-
-  \ControlFlowTok{while}\NormalTok{ queue:}
-\NormalTok{    current\_vertex }\OperatorTok{=}\NormalTok{ queue.popleft()}
-
-    \ControlFlowTok{if}\NormalTok{ current\_vertex }\KeywordTok{not} \KeywordTok{in}\NormalTok{ visited:}
-      \CommentTok{\# Process the current vertex}
-      \BuiltInTok{print}\NormalTok{(current\_vertex, end}\OperatorTok{=}\StringTok{\textquotesingle{} \textquotesingle{}}\NormalTok{)}
-\NormalTok{      visited.add(current\_vertex)}
-
-      \CommentTok{\# Enqueue unvisited neighbors}
-      \ControlFlowTok{for}\NormalTok{ neighbor }\KeywordTok{in}\NormalTok{ graph.graph.get(current\_vertex, []):}
-        \ControlFlowTok{if}\NormalTok{ neighbor }\KeywordTok{not} \KeywordTok{in}\NormalTok{ visited:}
-\NormalTok{          queue.append(neighbor)}
-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}[fragile]{Implementing Graphs and BFS}
-\protect\hypertarget{implementing-graphs-and-bfs-2}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\CommentTok{\# Represent graph from above}
-\NormalTok{ex\_graph }\OperatorTok{=}\NormalTok{ Graph()}
-\NormalTok{ex\_graph.add\_edge(}\DecValTok{1}\NormalTok{, [}\DecValTok{3}\NormalTok{, }\DecValTok{6}\NormalTok{])}
-\NormalTok{ex\_graph.add\_edge(}\DecValTok{3}\NormalTok{, [}\DecValTok{10}\NormalTok{, }\DecValTok{6}\NormalTok{])}
-\NormalTok{ex\_graph.add\_edge(}\DecValTok{6}\NormalTok{, [}\DecValTok{3}\NormalTok{, }\DecValTok{7}\NormalTok{])}
-\NormalTok{ex\_graph.add\_edge(}\DecValTok{10}\NormalTok{, [}\DecValTok{3}\NormalTok{, }\DecValTok{7}\NormalTok{])}
-\NormalTok{ex\_graph.add\_edge(}\DecValTok{7}\NormalTok{, [}\DecValTok{10}\NormalTok{, }\DecValTok{6}\NormalTok{])}
-
-\CommentTok{\# Perform BFS starting from vertex 1}
-\NormalTok{bfs(ex\_graph, }\DecValTok{1}\NormalTok{)}
-\end{Highlighting}
-\end{Shaded}
-
-\begin{verbatim}
-1 3 6 10 7 
-\end{verbatim}
-\end{frame}
-
-\begin{frame}[fragile]{Recursive Graph Search: Preorder Traversal}
-\protect\hypertarget{recursive-graph-search-preorder-traversal}{}
-\begin{itemize}
-\tightlist
-\item
-  Using the same \texttt{Graph} class, let's implement preorder
-  traversal
-\end{itemize}
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\KeywordTok{def}\NormalTok{ recursive\_preorder\_traversal(graph, start, visited}\OperatorTok{=}\VariableTok{None}\NormalTok{):}
-  \ControlFlowTok{if}\NormalTok{ visited }\KeywordTok{is} \VariableTok{None}\NormalTok{:}
-\NormalTok{    visited }\OperatorTok{=} \BuiltInTok{set}\NormalTok{()}
-
-  \CommentTok{\# Process the current vertex}
-  \BuiltInTok{print}\NormalTok{(start, end}\OperatorTok{=}\StringTok{\textquotesingle{} \textquotesingle{}}\NormalTok{)}
-\NormalTok{  visited.add(start)}
-
-    \CommentTok{\# Recursive traversal of neighbors}
-  \ControlFlowTok{for}\NormalTok{ neighbor }\KeywordTok{in}\NormalTok{ graph.graph.get(start, []):}
-    \ControlFlowTok{if}\NormalTok{ neighbor }\KeywordTok{not} \KeywordTok{in}\NormalTok{ visited:}
-\NormalTok{      recursive\_preorder\_traversal(graph, neighbor, visited)}
-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}[fragile]{Recursive Graph Search: Preorder Traversal}
-\protect\hypertarget{recursive-graph-search-preorder-traversal-1}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\NormalTok{bfs(ex\_graph, }\DecValTok{1}\NormalTok{)}
-\end{Highlighting}
-\end{Shaded}
-
-\begin{verbatim}
-1 3 6 10 7 
-\end{verbatim}
-\end{frame}
-
-\hypertarget{nearest-neighour-problem}{%
-\section{Nearest Neighour Problem}\label{nearest-neighour-problem}}
-
-\begin{frame}{Nearest Neighbour Problem}
-\protect\hypertarget{nearest-neighbour-problem}{}
-\begin{itemize}
-\item
-  As you may have encountered already, machine learning and statistical
-  methods often depend on finding the nearest neighbor to a data point
-
-  \begin{itemize}
-  \tightlist
-  \item
-    K-nearest neighbors regression, propensity score matching
-  \end{itemize}
-\item
-  In a \(k\) dimensional space, if we conduct a linear search for
-  points, the running time will be \(O(kn)\) for \(n\) data points.
-\item
-  Can we do better?
-\end{itemize}
-\end{frame}
-
-\begin{frame}{k-d Trees}
-\protect\hypertarget{k-d-trees}{}
-\begin{itemize}
-\item
-  k-d trees is short for k dimensional tree (notation is a bit
-  unfortunate, different K than KNN)
-
-  \begin{itemize}
-  \tightlist
-  \item
-    It is useful for multidimensional searches
-  \end{itemize}
-\item
-  Let's discuss the properties of k-d trees and why they work
-\item
-  Binary tree where each node represents an axis-aligned hyperrectangle
-  in the k-dimensional space
-
-  \begin{itemize}
-  \tightlist
-  \item
-    hyperrectangle: rectangle in higher dimensions
-  \end{itemize}
-\item
-  Nodes in the left subtree have coordinates less than the splitting
-  dimension of the current node, while nodes in the right subtree have
-  coordinates greater than the splitting dimension.
-\item
-  At each level of the tree, a specific dimension is chosen to split the
-  data. The choice of dimension alternates as we traverse down the tree.
-\item
-  Each leaf represents a single point in the k-dimensional space
-\end{itemize}
-\end{frame}
-
-\begin{frame}{k-d Trees Animation}
-\protect\hypertarget{k-d-trees-animation}{}
-\url{https://commons.wikimedia.org/wiki/File:KDTree-animation.gif}
-\end{frame}
-
-\begin{frame}{Applications and Issues}
-\protect\hypertarget{applications-and-issues}{}
-\begin{itemize}
-\item
-  Notice k-d trees can also find values within a certain range very
-  quickly, not just a specific point
-\item
-  GIS (geographic information systems) queries
-\item
-  KNN algorithm
-\item
-  Computer graphics, such as ray tracing to facilitate efficient space
-  partitioning
-\item
-  Issues occur in high-dimensional spaces and trees can become
-  imbalanced
-\end{itemize}
-\end{frame}
-
-\hypertarget{recommended-problems-and-references}{%
-\section{Recommended Problems and
-References}\label{recommended-problems-and-references}}
-
-\begin{frame}{Recommended Readings}
-\protect\hypertarget{recommended-readings}{}
-\begin{itemize}
-\item
-  Bhargava: Chapter 6
-\item
-  Bhargava: Chapter 11, pages 203 to 206 about Trees
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Recommended Problems}
-\protect\hypertarget{recommended-problems}{}
-\begin{itemize}
-\item
-  Cormen: Chapter 10 exercises
-
-  \begin{itemize}
-  \tightlist
-  \item
-    10.3-1, 10.3-2, 10.3-3
-  \end{itemize}
-\item
-  Bhargava: Chapter 6 exercises
-
-  \begin{itemize}
-  \tightlist
-  \item
-    6.1 to 6.5
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Recommended Problems}
-\protect\hypertarget{recommended-problems-1}{}
-\begin{itemize}
-\item
-  Implement preorder, postorder, and level order traversal. Determine
-  the time and space complexity in each case
-\item
-  Implement a function that find an element in a BST and deletes it. The
-  descendants of the deleted node are given to the deleted node's
-  parent.
-\item
-  Using the \texttt{graph} class from the slides, implement BST search
-  such that it stops and tell you the distance the node is from the
-  starting point.
-
-  \begin{itemize}
-  \item
-    For instance, if we searched for 7 in the graph given in the slides,
-    it would return \texttt{"Found!\ Distance\ 2"}.
-  \item
-    If we searched for 100 in the graph, it would return
-    \texttt{"Not\ found!"}
-  \end{itemize}
-\item
-  Implement postorder graph traversal using the \texttt{graph} class
-  from the slides.
-\item
-  Implement a function using recursion to find the sum heterogeneous
-  nested lists such as {[}{[}1, {[}2{]}{]}, {[}{[}{[}3{]}{]}{]}, 4,
-  {[}{[}5, 6{]}, {[}{[}{[}7{]}{]}{]}{]}{]}.
-\end{itemize}
-\end{frame}
-
-\begin{frame}{References}
-\protect\hypertarget{references}{}
-\begin{itemize}
-\item
-  Bhargava, A. Y. (2016). \emph{Grokking algorithms: An illustrated
-  guide for programmers and other curious people.} Manning. Chapter 6,
-  10, 11.
-\item
-  Cormen, T. H. (Ed.). (2009). \emph{Introduction to algorithms} (3rd
-  ed). MIT Press. Chapter 12 and 20.
-\item
-  Horton, D., \& Liu, D. (2023, November 19). \emph{CSC148 Lecture
-  Notes}.
-  https://www.teach.cs.toronto.edu/\textasciitilde csc148h/winter/notes/
-\end{itemize}
-\end{frame}
-
-
-
-\end{document}
diff --git a/slides-resources/4_recursive-ds/4_recursive-ds.toc b/slides-resources/4_recursive-ds/4_recursive-ds.toc
deleted file mode 100644
index 111d446..0000000
--- a/slides-resources/4_recursive-ds/4_recursive-ds.toc
+++ /dev/null
@@ -1,5 +0,0 @@
-\beamer@sectionintoc {1}{Trees}{3}{0}{1}
-\beamer@sectionintoc {2}{Binary Search Trees}{15}{0}{2}
-\beamer@sectionintoc {3}{Graphs}{20}{0}{3}
-\beamer@sectionintoc {4}{Nearest Neighour Problem}{37}{0}{4}
-\beamer@sectionintoc {5}{Recommended Problems and References}{42}{0}{5}
diff --git a/slides-resources/4_recursive-ds/4_recursive-ds.vrb b/slides-resources/4_recursive-ds/4_recursive-ds.vrb
deleted file mode 100644
index 31bb525..0000000
--- a/slides-resources/4_recursive-ds/4_recursive-ds.vrb
+++ /dev/null
@@ -1,31 +0,0 @@
-\frametitle{Recommended Problems}
-\protect\hypertarget{recommended-problems-1}{}
-\begin{itemize}
-\item
-  Implement preorder, postorder, and level order traversal. Determine
-  the time and space complexity in each case
-\item
-  Implement a function that find an element in a BST and deletes it. The
-  descendants of the deleted node are given to the deleted node's
-  parent.
-\item
-  Using the \texttt{graph} class from the slides, implement BST search
-  such that it stops and tell you the distance the node is from the
-  starting point.
-
-  \begin{itemize}
-  \item
-    For instance, if we searched for 7 in the graph given in the slides,
-    it would return \texttt{"Found!\ Distance\ 2"}.
-  \item
-    If we searched for 100 in the graph, it would return
-    \texttt{"Not\ found!"}
-  \end{itemize}
-\item
-  Implement postorder graph traversal using the \texttt{graph} class
-  from the slides.
-\item
-  Implement a function using recursion to find the sum heterogeneous
-  nested lists such as {[}{[}1, {[}2{]}{]}, {[}{[}{[}3{]}{]}{]}, 4,
-  {[}{[}5, 6{]}, {[}{[}{[}7{]}{]}{]}{]}{]}.
-\end{itemize}
diff --git a/slides-resources/5_optimization/.Rhistory b/slides-resources/5_optimization/.Rhistory
deleted file mode 100644
index dbc71ec..0000000
--- a/slides-resources/5_optimization/.Rhistory
+++ /dev/null
@@ -1,3 +0,0 @@
-python
-reticulate::repl_python()
-- Suppose we want to hold as many classes in a classroom as possible.
diff --git a/slides-resources/5_optimization/5_optimization.aux b/slides-resources/5_optimization/5_optimization.aux
deleted file mode 100644
index 7738a7d..0000000
--- a/slides-resources/5_optimization/5_optimization.aux
+++ /dev/null
@@ -1,91 +0,0 @@
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diff --git a/slides-resources/5_optimization/5_optimization.pdf b/slides-resources/5_optimization/5_optimization.pdf
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----
-title: "Optimization"
-format: 
-  beamer:
-    institute: Data Sciences Institute, UofT
-    theme: Boadilla
-    colortheme: rose
-execute:
-  echo: true
-editor: visual
-author: Salaar Liaqat
----
-
-## Outline
-
--   Setting up an Optimization Problem
-
--   Dynamic Programming
-
-# Setting up an Optimization Problem
-
-## Types of Optimization Problems
-
--   *Optimization* refers to maximizing or minimizing a function with respect to its inputs
-
--   Continuous optimization is when all the variables in the problem are continuous
-
--   Discrete optimization occurs when some or all of the variables in the problem are discrete
-
-    -   Continuous: how many hours should workers in a factory work to maximize profits?
-
-    -   Discrete: how do I allocate TAs to teach within a department?
-
-## Autocorrect Example
-
--   Autocorrect in an optimization algorithm. It has two parts
-
-    -   We need a list of known words and their use frequency
-
-    -   Classify errors are either: add a letter, remove a letter, substitute a letter, or switched two adjacent letters
-
--   We quantify the error distance as the of errors in a string.
-
-    -   "ovon" -\> "oven" is error distance 1
-
-    -   "ovvvn" -\> "ovven" -\> "oven" is error distance 2
-
-## Autocorrect Example
-
-1.  Check whether a word is in the dictionary
-
-2.  If the word is not in the dictionary, generate words that are error distance 1 or 2 from the given word
-
-3.  Rank the most likely correction given the error distance and use frequency
-
--   "thene" could be "then" or "the," but but "the" is more common
-
-## Autocorrect Example
-
-What are the steps to model the problem?
-
--   We have the specification of possible inputs
-
-    -   Text, discrete
-
--   The *objective function* is the function you are trying to maximize more minimize
-
-    -   Function with 2 variables: error distance and frequency
-
--   Are we maximizing or minimizing the objective function
-
-    -   Minimize error distance and maximize frequency
-
--   Identify the *constraints* in the problem
-
-    -   Only looking for words in the dictionary, only looking for words with error distance 1 or 2
-
-## Shortest Path in a Graph Example
-
--   Finding the shortest path between two nodes on a graph is a discrete optimization problem
-
--   The range of inputs are all possible paths from A to B
-
--   The objective function is the length of the path
-
--   We are minimizing the objective function
-
--   And there are no constraints
-
-## Brute Force
-
-Consider the following problems and proposed solutions
-
--   You want to consume all necessary nutrients and calories at the lowest cost. So, you find all valid combinations of foods and find their cost.
-
-    -   If there are 10 foods, and 15 nutritional categories, then there are $2^{10 \times 15} = 1.42 \times 10^{45}$ combinations to evaluate
-
-    -   We will fix this with *linear programming*
-
--   You are robbing a store but the escape vent can only carry 4 kg of goods. To steal the maximum money's worth of goods, you calculate every set of goods and find the one giving the most value
-
-    -   If there are 3 goods in the store, then there are 8 combinations. But with 4 goods, there are 16 combinations. This solution is $O(2^n)$ time.
-
-    -   We will fix this with *dynamic programming*
-
-# Linear Programming
-
-## Linear Programming
-
--   Linear programming (LP) takes advantage of a program being linear. (what does that mean?)
-
--   If we're considering a food that already fills one nutrition category, we can eliminate all other combinations that use the food
-
-    -   Sounds obvious, but brute forcing doesn't consider this!
-
--   By this process of elimination, we make the problem much faster to solve.
-
-## Implementing LP in Python
-
-Let's consider a very simple diet problem where the goal is to minimize the cost. There are 3 foods: apples (\$3), bananas (\$1), and oranges (\$3). We want to meet 3 constraints: of vitamin A, a number of vitamin B, and a number of calories.
-
--   Assume there is no upper limit on calories or vitamins
-
--   The PuLP library is a popular linear programming library to do this in Python
-
-```{python}
-from pulp import LpProblem, LpMinimize, LpVariable, lpSum
-```
-
-## Implementing LP in Python
-
-```{python}
-#| output: false
-diet_problem = LpProblem("Diet_Problem", LpMinimize)
-
-# Define output variables
-x1 = LpVariable("Apples", lowBound=0)
-x2 = LpVariable("Bananas", lowBound=0)
-x3 = LpVariable("Oranges", lowBound=0)
-
-# Define objective function (minimize cost)
-diet_problem += 3 * x1 + x2 + 3 * x3, "Total_Cost"
-
-# Define nutritional constraints
-diet_problem += 50 * x1 + 120 * x2 + 60 * x3 >= 2000, "Calories"
-diet_problem += 2 * x1 + 3 * x2 + 5 * x3>= 40, "Vitamin A"
-diet_problem += 12 * x1 + x2 + 2 * x3>= 50, "Vitamin B"
-
-diet_problem.solve()
-```
-
-## Implementing LP in Python
-
-```{python}
-print("Optimal Diet:")
-print(f"Apples: {round(x1.value(), 2)} units")
-print(f"Bananas: {round(x2.value(), 2)} units")
-print(f"Oranges: {round(x3.value(), 2)} units")
-print(f"Total Cost: {round(diet_problem.objective.value(), 2)}")
-```
-
-# Dynamic Programming
-
-## Problem
-
-The escape vent can carry only 4 kg of goods. The items are:
-
--   Stereo: \$3000, 4 kg
-
--   Laptop: \$2000, 3 kg
-
--   Guitar: \$1500, 1 kg
-
-We've established the brute force is not a valid general solution (although feasible in this case)
-
--   The idea behind dynamic programming is that we'll solve subproblems that will lead to a solution to the big problem. We can pack items starting by considering smaller, sub backpacks
-
-## Guitar Row
-
-::: columns
-::: {.column width="50%"}
--   Each dynamic programming problem starts with a grid
-
--   Each cell contains a list of items that can fit at that point
-
--   For cell Guitar 1, a guitar will fit there. It will also fit in cell Guitar 2, 3, 4
-
--   Sounds redundant, but let's keep going
-:::
-
-::: {.column width="50%"}
-![](images/dynamic.png)
-:::
-:::
-
-## Stereo Row
-
-::: columns
-::: {.column width="50%"}
--   In the second row, we can steal the stereo or the guitar.
-
--   At 1 kg, you can only steal the guitar, same as for every other cell until Stereo 4, at which point you can steal the stereo and only the stereo.
-:::
-
-::: {.column width="50%"}
-![](images/dynamic.png)
-:::
-:::
-
-## Laptop Row
-
-::: columns
-::: {.column width="50%"}
--   Now we can steal all 3 items
-
--   In the first two columns, we still can only steal the guitar. But in Laptop 3, we can steal the laptop
-
--   Laptop 4 is the interesting step. We could steal only the stereo, or the laptop and something else for 1 kg. What is that 1 kg item?
-
--   According to the above row, the max value for 1 kg is the guitar!
-:::
-
-::: {.column width="50%"}
-![](images/dynamic.png)
-:::
-:::
-
-## Solution
-
-::: columns
-::: {.column width="50%"}
--   If we stole the guitar and laptop, the total value is 3500, which is greater than just stealing the stereo
-
--   Thus, we should steal guitar and laptop
-:::
-
-::: {.column width="50%"}
-![](images/dynamic.png)
-:::
-:::
-
-## Formula for each cell
-
--   We skipped some very trivial steps in calculating cells aside from the last one
-
--   Here's the explicit formula to calculate each cell's value
-
-Let $i$ be the row and $j$ be the column.
-
-$$
-\text{cell}[i][j] = \max
-    \begin{cases}
-        \text{the previous max at cell}[i-1][j]\\
-        \text{value of current item + value of remaining space}
-    \end{cases}
-$$
-
-The value of remaining space is cell\[i-1\]\[j-item's weight\]
-
-## Python Implementation
-
-```{python}
-def initialize_table(rows, cols):
-  return [[0] * cols for _ in range(rows)]
-```
-
-## Python Implementation
-
-```{python}
-def knapsack_dynamic_programming(values, weights, capacity):
-  n = len(values)
-  dp = initialize_table(n + 1, capacity + 1)
-
-  # Fill the table using dynamic programming
-  for i in range(1, n + 1):
-    for w in range(capacity + 1):
-      # Include the current item if it fits in the knapsack
-      if weights[i - 1] <= w:
-        dp[i][w] = max(dp[i - 1][w], \
-        values[i - 1] + dp[i - 1][w - weights[i - 1]])
-      else:
-        dp[i][w] = dp[i - 1][w]
-
-  selected_items = traceback(dp, values, weights, capacity)
-
-  return dp[n][capacity], selected_items
-```
-
-## Python Implementation
-
-```{python}
-def traceback(dp, values, weights, capacity):
-  selected_items = []
-  i, w = len(dp) - 1, capacity
-
-  while i > 0 and w > 0:
-    if dp[i][w] != dp[i - 1][w]:
-      selected_items.append(i - 1)
-      w -= weights[i - 1]
-      i -= 1
-
-  selected_items.reverse()
-  return selected_items
-```
-
-## Python Implementation
-
-```{python}
-values = [3000, 2000, 1500]
-weights = [4, 3, 1]
-capacity = 4
-
-max_value, selected_items = \
-knapsack_dynamic_programming(values, weights, capacity)
-
-print("Maximum value:", max_value)
-print("Selected items:", selected_items)
-```
-
-## Live Coding
-
-Let a substring be *upper-lower* if for every letter of the alphabet that the string contains, it appears both in uppercase and lowercase. For example, `aaA` is upper-lower because it has both "A" and "a." `aAbb` is not upper-lower because it lacks an upper case "B."
-
-Given string, return the longest substring that is *upper-lower.*
-
-### Example
-
-```{python}
-#| eval: false
-# INPUT
-string = "AqeQEfa"
-# OUTPUT
-"qeQE"
-```
-
-# Recommended Problems and References
-
-## Recommended Problems and Readings
-
--   Cormen (highly optional):
-
--   Chapter 14, more advanced dynamic programming
-
--   Chapter 29, more advanced linear programming
-
--   Bhargava: Chapter 9 exercises
-
-    -   9.1, 9.2
-
-    -   Read the knapsack problem FAQs on page 171
-
-    -   Follow the example about longest common substring on page 178
-
-## Recommended Problems
-
--   Write the code to brute force the diet problem. Compare the run times using the `timeit` library.
-
--   Modify the code from the slide such that there is an upper bound for calories and vitamins.
-
--   Page 17 of Bhargava covered the travelling sales person problem. Is it possible to improve the proposed solution using any method we learned today?
-
-## References
-
--   Bhargava, A. Y. (2016). *Grokking algorithms: An illustrated guide for programmers and other curious people.* Manning. Chapter 1.
-
--   Cormen, T. H. (Ed.). (2009). *Introduction to algorithms* (3rd ed). MIT Press. Chapter 1 and 3.
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-  pdftitle={Optimization},
-  pdfauthor={Salaar Liaqat},
-  hidelinks,
-  pdfcreator={LaTeX via pandoc}}
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-\title{Optimization}
-\author{Salaar Liaqat}
-\date{}
-\institute{Data Sciences Institute, UofT}
-
-\begin{document}
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-
-\begin{frame}{Outline}
-\protect\hypertarget{outline}{}
-\begin{itemize}
-\item
-  Setting up an Optimization Problem
-\item
-  Dynamic Programming
-\end{itemize}
-\end{frame}
-
-\hypertarget{setting-up-an-optimization-problem}{%
-\section{Setting up an Optimization
-Problem}\label{setting-up-an-optimization-problem}}
-
-\begin{frame}{Types of Optimization Problems}
-\protect\hypertarget{types-of-optimization-problems}{}
-\begin{itemize}
-\item
-  \emph{Optimization} refers to maximizing or minimizing a function with
-  respect to its inputs
-\item
-  Continuous optimization is when all the variables in the problem are
-  continuous
-\item
-  Discrete optimization occurs when some or all of the variables in the
-  problem are discrete
-
-  \begin{itemize}
-  \item
-    Continuous: how many hours should workers in a factory work to
-    maximize profits?
-  \item
-    Discrete: how do I allocate TAs to teach within a department?
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Autocorrect Example}
-\protect\hypertarget{autocorrect-example}{}
-\begin{itemize}
-\item
-  Autocorrect in an optimization algorithm. It has two parts
-
-  \begin{itemize}
-  \item
-    We need a list of known words and their use frequency
-  \item
-    Classify errors are either: add a letter, remove a letter,
-    substitute a letter, or switched two adjacent letters
-  \end{itemize}
-\item
-  We quantify the error distance as the of errors in a string.
-
-  \begin{itemize}
-  \item
-    ``ovon'' -\textgreater{} ``oven'' is error distance 1
-  \item
-    ``ovvvn'' -\textgreater{} ``ovven'' -\textgreater{} ``oven'' is
-    error distance 2
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Autocorrect Example}
-\protect\hypertarget{autocorrect-example-1}{}
-\begin{enumerate}
-\item
-  Check whether a word is in the dictionary
-\item
-  If the word is not in the dictionary, generate words that are error
-  distance 1 or 2 from the given word
-\item
-  Rank the most likely correction given the error distance and use
-  frequency
-\end{enumerate}
-
-\begin{itemize}
-\tightlist
-\item
-  ``thene'' could be ``then'' or ``the,'' but but ``the'' is more common
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Autocorrect Example}
-\protect\hypertarget{autocorrect-example-2}{}
-What are the steps to model the problem?
-
-\begin{itemize}
-\item
-  We have the specification of possible inputs
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Text, discrete
-  \end{itemize}
-\item
-  The \emph{objective function} is the function you are trying to
-  maximize more minimize
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Function with 2 variables: error distance and frequency
-  \end{itemize}
-\item
-  Are we maximizing or minimizing the objective function
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Minimize error distance and maximize frequency
-  \end{itemize}
-\item
-  Identify the \emph{constraints} in the problem
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Only looking for words in the dictionary, only looking for words
-    with error distance 1 or 2
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Shortest Path in a Graph Example}
-\protect\hypertarget{shortest-path-in-a-graph-example}{}
-\begin{itemize}
-\item
-  Finding the shortest path between two nodes on a graph is a discrete
-  optimization problem
-\item
-  The range of inputs are all possible paths from A to B
-\item
-  The objective function is the length of the path
-\item
-  We are minimizing the objective function
-\item
-  And there are no constraints
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Brute Force}
-\protect\hypertarget{brute-force}{}
-Consider the following problems and proposed solutions
-
-\begin{itemize}
-\item
-  You want to consume all necessary nutrients and calories at the lowest
-  cost. So, you find all valid combinations of foods and find their
-  cost.
-
-  \begin{itemize}
-  \item
-    If there are 10 foods, and 15 nutritional categories, then there are
-    \(2^{10 \times 15} = 1.42 \times 10^{45}\) combinations to evaluate
-  \item
-    We will fix this with \emph{linear programming}
-  \end{itemize}
-\item
-  You are robbing a store but the escape vent can only carry 4 kg of
-  goods. To steal the maximum money's worth of goods, you calculate
-  every set of goods and find the one giving the most value
-
-  \begin{itemize}
-  \item
-    If there are 3 goods in the store, then there are 8 combinations.
-    But with 4 goods, there are 16 combinations. This solution is
-    \(O(2^n)\) time.
-  \item
-    We will fix this with \emph{dynamic programming}
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\hypertarget{linear-programming}{%
-\section{Linear Programming}\label{linear-programming}}
-
-\begin{frame}{Linear Programming}
-\protect\hypertarget{linear-programming-1}{}
-\begin{itemize}
-\item
-  Linear programming (LP) takes advantage of a program being linear.
-  (what does that mean?)
-\item
-  If we're considering a food that already fills one nutrition category,
-  we can eliminate all other combinations that use the food
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Sounds obvious, but brute forcing doesn't consider this!
-  \end{itemize}
-\item
-  By this process of elimination, we make the problem much faster to
-  solve.
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Implementing LP in Python}
-\protect\hypertarget{implementing-lp-in-python}{}
-Let's consider a very simple diet problem where the goal is to minimize
-the cost. There are 3 foods: apples (\$3), bananas (\$1), and oranges
-(\$3). We want to meet 3 constraints: of vitamin A, a number of vitamin
-B, and a number of calories.
-
-\begin{itemize}
-\item
-  Assume there is no upper limit on calories or vitamins
-\item
-  The PuLP library is a popular linear programming library to do this in
-  Python
-\end{itemize}
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\ImportTok{from}\NormalTok{ pulp }\ImportTok{import}\NormalTok{ LpProblem, LpMinimize, LpVariable, lpSum}
-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}[fragile]{Implementing LP in Python}
-\protect\hypertarget{implementing-lp-in-python-1}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\NormalTok{diet\_problem }\OperatorTok{=}\NormalTok{ LpProblem(}\StringTok{"Diet\_Problem"}\NormalTok{, LpMinimize)}
-
-\CommentTok{\# Define output variables}
-\NormalTok{x1 }\OperatorTok{=}\NormalTok{ LpVariable(}\StringTok{"Apples"}\NormalTok{, lowBound}\OperatorTok{=}\DecValTok{0}\NormalTok{)}
-\NormalTok{x2 }\OperatorTok{=}\NormalTok{ LpVariable(}\StringTok{"Bananas"}\NormalTok{, lowBound}\OperatorTok{=}\DecValTok{0}\NormalTok{)}
-\NormalTok{x3 }\OperatorTok{=}\NormalTok{ LpVariable(}\StringTok{"Oranges"}\NormalTok{, lowBound}\OperatorTok{=}\DecValTok{0}\NormalTok{)}
-
-\CommentTok{\# Define objective function (minimize cost)}
-\NormalTok{diet\_problem }\OperatorTok{+=} \DecValTok{3} \OperatorTok{*}\NormalTok{ x1 }\OperatorTok{+}\NormalTok{ x2 }\OperatorTok{+} \DecValTok{3} \OperatorTok{*}\NormalTok{ x3, }\StringTok{"Total\_Cost"}
-
-\CommentTok{\# Define nutritional constraints}
-\NormalTok{diet\_problem }\OperatorTok{+=} \DecValTok{50} \OperatorTok{*}\NormalTok{ x1 }\OperatorTok{+} \DecValTok{120} \OperatorTok{*}\NormalTok{ x2 }\OperatorTok{+} \DecValTok{60} \OperatorTok{*}\NormalTok{ x3 }\OperatorTok{\textgreater{}=} \DecValTok{2000}\NormalTok{, }\StringTok{"Calories"}
-\NormalTok{diet\_problem }\OperatorTok{+=} \DecValTok{2} \OperatorTok{*}\NormalTok{ x1 }\OperatorTok{+} \DecValTok{3} \OperatorTok{*}\NormalTok{ x2 }\OperatorTok{+} \DecValTok{5} \OperatorTok{*}\NormalTok{ x3}\OperatorTok{\textgreater{}=} \DecValTok{40}\NormalTok{, }\StringTok{"Vitamin A"}
-\NormalTok{diet\_problem }\OperatorTok{+=} \DecValTok{12} \OperatorTok{*}\NormalTok{ x1 }\OperatorTok{+}\NormalTok{ x2 }\OperatorTok{+} \DecValTok{2} \OperatorTok{*}\NormalTok{ x3}\OperatorTok{\textgreater{}=} \DecValTok{50}\NormalTok{, }\StringTok{"Vitamin B"}
-
-\NormalTok{diet\_problem.solve()}
-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}[fragile]{Implementing LP in Python}
-\protect\hypertarget{implementing-lp-in-python-2}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\BuiltInTok{print}\NormalTok{(}\StringTok{"Optimal Diet:"}\NormalTok{)}
-\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Apples: }\SpecialCharTok{\{}\BuiltInTok{round}\NormalTok{(x1.value(), }\DecValTok{2}\NormalTok{)}\SpecialCharTok{\}}\SpecialStringTok{ units"}\NormalTok{)}
-\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Bananas: }\SpecialCharTok{\{}\BuiltInTok{round}\NormalTok{(x2.value(), }\DecValTok{2}\NormalTok{)}\SpecialCharTok{\}}\SpecialStringTok{ units"}\NormalTok{)}
-\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Oranges: }\SpecialCharTok{\{}\BuiltInTok{round}\NormalTok{(x3.value(), }\DecValTok{2}\NormalTok{)}\SpecialCharTok{\}}\SpecialStringTok{ units"}\NormalTok{)}
-\BuiltInTok{print}\NormalTok{(}\SpecialStringTok{f"Total Cost: }\SpecialCharTok{\{}\BuiltInTok{round}\NormalTok{(diet\_problem.objective.value(), }\DecValTok{2}\NormalTok{)}\SpecialCharTok{\}}\SpecialStringTok{"}\NormalTok{)}
-\end{Highlighting}
-\end{Shaded}
-
-\begin{verbatim}
-Optimal Diet:
-Apples: 2.88 units
-Bananas: 15.47 units
-Oranges: 0.0 units
-Total Cost: 24.1
-\end{verbatim}
-\end{frame}
-
-\hypertarget{dynamic-programming}{%
-\section{Dynamic Programming}\label{dynamic-programming}}
-
-\begin{frame}{Problem}
-\protect\hypertarget{problem}{}
-The escape vent can carry only 4 kg of goods. The items are:
-
-\begin{itemize}
-\item
-  Stereo: \$3000, 4 kg
-\item
-  Laptop: \$2000, 3 kg
-\item
-  Guitar: \$1500, 1 kg
-\end{itemize}
-
-We've established the brute force is not a valid general solution
-(although feasible in this case)
-
-\begin{itemize}
-\tightlist
-\item
-  The idea behind dynamic programming is that we'll solve subproblems
-  that will lead to a solution to the big problem. We can pack items
-  starting by considering smaller, sub backpacks
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Guitar Row}
-\protect\hypertarget{guitar-row}{}
-\begin{columns}[T]
-\begin{column}{0.5\textwidth}
-\begin{itemize}
-\item
-  Each dynamic programming problem starts with a grid
-\item
-  Each cell contains a list of items that can fit at that point
-\item
-  For cell Guitar 1, a guitar will fit there. It will also fit in cell
-  Guitar 2, 3, 4
-\item
-  Sounds redundant, but let's keep going
-\end{itemize}
-\end{column}
-
-\begin{column}{0.5\textwidth}
-\includegraphics{images/dynamic.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{Stereo Row}
-\protect\hypertarget{stereo-row}{}
-\begin{columns}[T]
-\begin{column}{0.5\textwidth}
-\begin{itemize}
-\item
-  In the second row, we can steal the stereo or the guitar.
-\item
-  At 1 kg, you can only steal the guitar, same as for every other cell
-  until Stereo 4, at which point you can steal the stereo and only the
-  stereo.
-\end{itemize}
-\end{column}
-
-\begin{column}{0.5\textwidth}
-\includegraphics{images/dynamic.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{Laptop Row}
-\protect\hypertarget{laptop-row}{}
-\begin{columns}[T]
-\begin{column}{0.5\textwidth}
-\begin{itemize}
-\item
-  Now we can steal all 3 items
-\item
-  In the first two columns, we still can only steal the guitar. But in
-  Laptop 3, we can steal the laptop
-\item
-  Laptop 4 is the interesting step. We could steal only the stereo, or
-  the laptop and something else for 1 kg. What is that 1 kg item?
-\item
-  According to the above row, the max value for 1 kg is the guitar!
-\end{itemize}
-\end{column}
-
-\begin{column}{0.5\textwidth}
-\includegraphics{images/dynamic.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{Solution}
-\protect\hypertarget{solution}{}
-\begin{columns}[T]
-\begin{column}{0.5\textwidth}
-\begin{itemize}
-\item
-  If we stole the guitar and laptop, the total value is 3500, which is
-  greater than just stealing the stereo
-\item
-  Thus, we should steal guitar and laptop
-\end{itemize}
-\end{column}
-
-\begin{column}{0.5\textwidth}
-\includegraphics{images/dynamic.png}
-\end{column}
-\end{columns}
-\end{frame}
-
-\begin{frame}{Formula for each cell}
-\protect\hypertarget{formula-for-each-cell}{}
-\begin{itemize}
-\item
-  We skipped some very trivial steps in calculating cells aside from the
-  last one
-\item
-  Here's the explicit formula to calculate each cell's value
-\end{itemize}
-
-Let \(i\) be the row and \(j\) be the column.
-
-\[
-\text{cell}[i][j] = \max
-    \begin{cases}
-        \text{the previous max at cell}[i-1][j]\\
-        \text{value of current item + value of remaining space}
-    \end{cases}
-\]
-
-The value of remaining space is cell{[}i-1{]}{[}j-item's weight{]}
-\end{frame}
-
-\begin{frame}[fragile]{Python Implementation}
-\protect\hypertarget{python-implementation}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\KeywordTok{def}\NormalTok{ initialize\_table(rows, cols):}
-  \ControlFlowTok{return}\NormalTok{ [[}\DecValTok{0}\NormalTok{] }\OperatorTok{*}\NormalTok{ cols }\ControlFlowTok{for}\NormalTok{ \_ }\KeywordTok{in} \BuiltInTok{range}\NormalTok{(rows)]}
-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}[fragile]{Python Implementation}
-\protect\hypertarget{python-implementation-1}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\KeywordTok{def}\NormalTok{ knapsack\_dynamic\_programming(values, weights, capacity):}
-\NormalTok{  n }\OperatorTok{=} \BuiltInTok{len}\NormalTok{(values)}
-\NormalTok{  dp }\OperatorTok{=}\NormalTok{ initialize\_table(n }\OperatorTok{+} \DecValTok{1}\NormalTok{, capacity }\OperatorTok{+} \DecValTok{1}\NormalTok{)}
-
-  \CommentTok{\# Fill the table using dynamic programming}
-  \ControlFlowTok{for}\NormalTok{ i }\KeywordTok{in} \BuiltInTok{range}\NormalTok{(}\DecValTok{1}\NormalTok{, n }\OperatorTok{+} \DecValTok{1}\NormalTok{):}
-    \ControlFlowTok{for}\NormalTok{ w }\KeywordTok{in} \BuiltInTok{range}\NormalTok{(capacity }\OperatorTok{+} \DecValTok{1}\NormalTok{):}
-      \CommentTok{\# Include the current item if it fits in the knapsack}
-      \ControlFlowTok{if}\NormalTok{ weights[i }\OperatorTok{{-}} \DecValTok{1}\NormalTok{] }\OperatorTok{\textless{}=}\NormalTok{ w:}
-\NormalTok{        dp[i][w] }\OperatorTok{=} \BuiltInTok{max}\NormalTok{(dp[i }\OperatorTok{{-}} \DecValTok{1}\NormalTok{][w], }\OperatorTok{\textbackslash{}}
-\NormalTok{        values[i }\OperatorTok{{-}} \DecValTok{1}\NormalTok{] }\OperatorTok{+}\NormalTok{ dp[i }\OperatorTok{{-}} \DecValTok{1}\NormalTok{][w }\OperatorTok{{-}}\NormalTok{ weights[i }\OperatorTok{{-}} \DecValTok{1}\NormalTok{]])}
-      \ControlFlowTok{else}\NormalTok{:}
-\NormalTok{        dp[i][w] }\OperatorTok{=}\NormalTok{ dp[i }\OperatorTok{{-}} \DecValTok{1}\NormalTok{][w]}
-
-\NormalTok{  selected\_items }\OperatorTok{=}\NormalTok{ traceback(dp, values, weights, capacity)}
-
-  \ControlFlowTok{return}\NormalTok{ dp[n][capacity], selected\_items}
-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}[fragile]{Python Implementation}
-\protect\hypertarget{python-implementation-2}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\KeywordTok{def}\NormalTok{ traceback(dp, values, weights, capacity):}
-\NormalTok{  selected\_items }\OperatorTok{=}\NormalTok{ []}
-\NormalTok{  i, w }\OperatorTok{=} \BuiltInTok{len}\NormalTok{(dp) }\OperatorTok{{-}} \DecValTok{1}\NormalTok{, capacity}
-
-  \ControlFlowTok{while}\NormalTok{ i }\OperatorTok{\textgreater{}} \DecValTok{0} \KeywordTok{and}\NormalTok{ w }\OperatorTok{\textgreater{}} \DecValTok{0}\NormalTok{:}
-    \ControlFlowTok{if}\NormalTok{ dp[i][w] }\OperatorTok{!=}\NormalTok{ dp[i }\OperatorTok{{-}} \DecValTok{1}\NormalTok{][w]:}
-\NormalTok{      selected\_items.append(i }\OperatorTok{{-}} \DecValTok{1}\NormalTok{)}
-\NormalTok{      w }\OperatorTok{{-}=}\NormalTok{ weights[i }\OperatorTok{{-}} \DecValTok{1}\NormalTok{]}
-\NormalTok{      i }\OperatorTok{{-}=} \DecValTok{1}
-
-\NormalTok{  selected\_items.reverse()}
-  \ControlFlowTok{return}\NormalTok{ selected\_items}
-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}[fragile]{Python Implementation}
-\protect\hypertarget{python-implementation-3}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\NormalTok{values }\OperatorTok{=}\NormalTok{ [}\DecValTok{3000}\NormalTok{, }\DecValTok{2000}\NormalTok{, }\DecValTok{1500}\NormalTok{]}
-\NormalTok{weights }\OperatorTok{=}\NormalTok{ [}\DecValTok{4}\NormalTok{, }\DecValTok{3}\NormalTok{, }\DecValTok{1}\NormalTok{]}
-\NormalTok{capacity }\OperatorTok{=} \DecValTok{4}
-
-\NormalTok{max\_value, selected\_items }\OperatorTok{=} \OperatorTok{\textbackslash{}}
-\NormalTok{knapsack\_dynamic\_programming(values, weights, capacity)}
-
-\BuiltInTok{print}\NormalTok{(}\StringTok{"Maximum value:"}\NormalTok{, max\_value)}
-\BuiltInTok{print}\NormalTok{(}\StringTok{"Selected items:"}\NormalTok{, selected\_items)}
-\end{Highlighting}
-\end{Shaded}
-
-\begin{verbatim}
-Maximum value: 3500
-Selected items: [1, 2]
-\end{verbatim}
-\end{frame}
-
-\begin{frame}[fragile]{Live Coding}
-\protect\hypertarget{live-coding}{}
-Let a substring be \emph{upper-lower} if for every letter of the
-alphabet that the string contains, it appears both in uppercase and
-lowercase. For example, \texttt{aaA} is upper-lower because it has both
-``A'' and ``a.'' \texttt{aAbb} is not upper-lower because it lacks an
-upper case ``B.''
-
-Given string, return the longest substring that is \emph{upper-lower.}
-
-\begin{block}{Example}
-\protect\hypertarget{example}{}
-\begin{Shaded}
-\begin{Highlighting}[]
-\CommentTok{\# INPUT}
-\NormalTok{string }\OperatorTok{=} \StringTok{"AqeQEfa"}
-\CommentTok{\# OUTPUT}
-\CommentTok{"qeQE"}
-\end{Highlighting}
-\end{Shaded}
-\end{block}
-\end{frame}
-
-\hypertarget{recommended-problems-and-references}{%
-\section{Recommended Problems and
-References}\label{recommended-problems-and-references}}
-
-\begin{frame}{Recommended Problems and Readings}
-\protect\hypertarget{recommended-problems-and-readings}{}
-\begin{itemize}
-\item
-  Cormen (highly optional):
-\item
-  Chapter 14, more advanced dynamic programming
-\item
-  Chapter 29, more advanced linear programming
-\item
-  Bhargava: Chapter 9 exercises
-
-  \begin{itemize}
-  \item
-    9.1, 9.2
-  \item
-    Read the knapsack problem FAQs on page 171
-  \item
-    Follow the example about longest common substring on page 178
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Recommended Problems}
-\protect\hypertarget{recommended-problems}{}
-\begin{itemize}
-\item
-  Write the code to brute force the diet problem. Compare the run times
-  using the \texttt{timeit} library.
-\item
-  Modify the code from the slide such that there is an upper bound for
-  calories and vitamins.
-\item
-  Page 17 of Bhargava covered the travelling sales person problem. Is it
-  possible to improve the proposed solution using any method we learned
-  today?
-\end{itemize}
-\end{frame}
-
-\begin{frame}{References}
-\protect\hypertarget{references}{}
-\begin{itemize}
-\item
-  Bhargava, A. Y. (2016). \emph{Grokking algorithms: An illustrated
-  guide for programmers and other curious people.} Manning. Chapter 1.
-\item
-  Cormen, T. H. (Ed.). (2009). \emph{Introduction to algorithms} (3rd
-  ed). MIT Press. Chapter 1 and 3.
-\end{itemize}
-\end{frame}
-
-
-
-\end{document}
diff --git a/slides-resources/5_optimization/5_optimization.toc b/slides-resources/5_optimization/5_optimization.toc
deleted file mode 100644
index 454eb07..0000000
--- a/slides-resources/5_optimization/5_optimization.toc
+++ /dev/null
@@ -1,4 +0,0 @@
-\beamer@sectionintoc {1}{Setting up an Optimization Problem}{3}{0}{1}
-\beamer@sectionintoc {2}{Linear Programming}{10}{0}{2}
-\beamer@sectionintoc {3}{Dynamic Programming}{15}{0}{3}
-\beamer@sectionintoc {4}{Recommended Problems and References}{27}{0}{4}
diff --git a/slides-resources/5_optimization/5_optimization.vrb b/slides-resources/5_optimization/5_optimization.vrb
deleted file mode 100644
index dd3f144..0000000
--- a/slides-resources/5_optimization/5_optimization.vrb
+++ /dev/null
@@ -1,14 +0,0 @@
-\frametitle{Recommended Problems}
-\protect\hypertarget{recommended-problems}{}
-\begin{itemize}
-\item
-  Write the code to brute force the diet problem. Compare the run times
-  using the \texttt{timeit} library.
-\item
-  Modify the code from the slide such that there is an upper bound for
-  calories and vitamins.
-\item
-  Page 17 of Bhargava covered the travelling sales person problem. Is it
-  possible to improve the proposed solution using any method we learned
-  today?
-\end{itemize}
diff --git a/slides-resources/5_optimization/texput.log b/slides-resources/5_optimization/texput.log
deleted file mode 100644
index 6079117..0000000
--- a/slides-resources/5_optimization/texput.log
+++ /dev/null
@@ -1,21 +0,0 @@
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-
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-End of file on the terminal!
-
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diff --git a/slides-resources/6_slow-code/6_slow-code.aux b/slides-resources/6_slow-code/6_slow-code.aux
deleted file mode 100644
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----
-title: "Why is my code slow?"
-format: 
-  beamer:
-    institute: Data Sciences Institute, UofT
-    theme: Boadilla
-    colortheme: rose
-execute:
-  echo: true
-editor: visual
-author: Salaar Liaqat
----
-
-## Outline
-
--   Caching, Memoization, and Vectorization
-
--   Parallel Computing
-
--   Greedy and Exhaustive Algorithms
-
--   Faster Implementations versus Faster Algorithms
-
-# Caching, Memoization, and Vectorization
-
-## Caching
-
--   *Caching* refers to storing things for later use
-
-    -   Your browser probably does by temporarily downloading page details on your local disk
-
-    -   Faster, reduces server load
-
-    -   Other examples include 3D rendering and saving common database queries
-
--   However, caching usually takes space in exchange for faster run times
-
--   The *space-time* trade off is a case where an algorithm trades increased space usage for faster runtimes
-
-## Memoization
-
--   *Memoization* refers to storing results of function calls to use for later
-
-    -   Specific method of caching
-
--   This is useful for methods with a lot of repeated computations
-
--   For instance, in our recursive Fibonacci number function.
-
--   `fib(12)` is called by `fib(13)`, `fib(14)` etc.
-
-    -   And `fib(3)` is called many many times
-
--   $F(5) = F(4) + F(3) = F(3) + F(2) + F(2) + F(1)$ Which calculates repeated subproblems
-
-## How Memoization Works
-
--   Since we store the results, each function call is only made once, making the time complexity $O(n)$, much better than $O(2^n)$ [^1]
-
--   Memoization can also avoid the maximum recursion depth error because the call stack is smaller
-
-[^1]: From Bhargava chapter 8
-
-![](images/memo.png){width="427"}
-
-## Memoization Python
-
-```{python}
-cache = {0: 0, 1: 1}
-
-def fib(n):
-  if n in cache:
-    return cache[n]
-  else:
-    cache[n] = fib(n - 1) + fib(n - 2)
-    return cache[n]
-```
-
-For the base cases, we replace calling `fib(0)` and `fib(1)` by getting the values from the dictionary
-
-## Memoization Python
-
--   We can use the `functools` library, which is included in the standard library (no pip install needed!)
-
-    -   `functools` does memoization for you!
-
--   We can use the `@cache` decorator, but the cached dictionary can grow to massive sizes
-
--   Instead, `@lru_cache(maxsize = n)` uses the LRU (least recently used) `n` computations
-
--   Alternatively, we can use `joblib` to store the memoized results in a file
-
-## Memoization Python
-
-```{python}
-from functools import lru_cache
-
-@lru_cache(maxsize=10)
-def fib_rec(n):
-  if n == 0 or n == 1:
-    return n
-  else:
-    return fib_rec(n-1) + fib_rec(n-2)
-```
-
-## Vectorized Operations
-
--   *Vectorization* is a technique of implementing array operations without for loops
-
--   We use functions defined by various modules that are highly optimized for the specific problem
-
--   NumPy provides a lot of functions that vectorized and are faster than for loops
-
-    -   Array add/subtract/multiply/divide by scalar
-
-    -   Sum of array
-
-    -   Max/min of array
-
--   Keep this in mind for some ML processes that are iterative, such as gradient descent
-
-## Why Vectorized Operations Work
-
--   Python (and R) are interpreted languages. There is no compiler and the languages are dynamic
-
--   C language, for instance, makes optimization at the compiler level (before execution) to speed up your code
-
--   Thus, NumPy implements arrays in C, which speeds things up
-
--   The other reason vectorization works in because of parallelization
-
-# Parallel Computing
-
-## Parallelization
-
-Compare the following codes. What are their run times?
-
-```{python}
-def fib(n):
-  if n <= 1:
-    return n
-  else:
-    return fib(n - 1) + fib(n - 2)
-```
-
-## Parallelization
-
-```{python}
-import numpy
-
-def add_one(n, x):
-  y = np.zeros(n)
-  for i in range(n):
-    y[i] = x[i] + 1
-        
-  return y
-```
-
-## Parallelization
-
--   Both are $O(n)$, but the second code chunk can be done in *parallel* because the $n$ computations are independent.
-
--   Fibonacci depends on the previous two values
-
--   The requirements for code to the parallelized and vectorized are similar, but not the same
-
--   The Numba library can help will parallelizing your code
-
--   Note parallel means the process takes place on one machine, but *distributed* means the computation is shared across many machines
-
-# Greedy and Exhaustive Algorithms
-
-## Greedy Approach (literally)
-
--   Let's revisit the knapsack problem, taking a different approach.
-
--   The items are:
-
-    -   Stereo: \$3000, 4 kg
-
-    -   Laptop: \$2000, 3 kg
-
-    -   Guitar: \$1500, 1 kg
-
--   If we follow the rule "get the most valuable item, then get second most valuable etc." we would make \$3000 by taking the stereo, which isn't the optimal \$3500
-
--   A *greedy algorithm* picks the optimal move at each step, which hopefully leads to the overall optimal solution
-
-    -   But it finds the solution in $O(n)$ time
-
-## Greedy Apporach
-
--   Let's say you could take fractions of an item and we tried the greedy approach
-
-    -   Peanuts: \$7/kg
-
-    -   Rice: \$5/kg
-
-    -   Tea: \$12/kg
-
--   We would take tea until it runs out, followed by peanuts and rice. This is the optimal solution in $O(n)$ time!
-
-## Classroom Scheduling Problem
-
--   Suppose we want to hold as many classes in a classroom as possible [^2]
-
-[^2]: From Bhargava chapter 8
-
-| Class        | Start   | End     |
-|--------------|---------|---------|
-| Yoga         | 9AM     | 10AM    |
-| Music Theory | 9:30AM  | 11AM    |
-| Painting     | 10AM    | 11AM    |
-| Algorithms   | 10:30AM | 11:30AM |
-| Calculus     | 11AM    | 12PM    |
-
-2 Minutes: write down a greedy algorithm to solve this problem
-
-## Classroom Scheduling Problem
-
-Algorithm
-
-1.  Pick the class that ends the soonest. This is the first class you’ll hold in this classroom
-
-2.  Now, you have to pick a class that starts after the first class. Again, pick the class that ends the soonest. This is the second class you’ll hold
-
-3.  Repeat the second step
-
-This not only produces the correct solution but also does so in $O(n)$ time, for $n$ classes!
-
-## Classroom Scheduling Problem
-
--   An alternative algorithm is the *exhaustive approach*
-
-    -   We try every combination of classes. At the end, we see which solution fits the most classes
-
-    -   We try every combination of items to steal. At the end, we see which solution has the most value
-
--   While brute forcing might sound always unnecessary, there are cases where it is needed to get the optimal solution
-
-    -   When performing subset selection for regression or decision tree, we can't guarantee the variables are uncorrelated. So forward/backward stepwise selection isn't guaranteed to produce the best outcome
-    -   More on this in a few slides
-
--   2 minutes: what is the time complexity of best subset selection?
-
-## Greedy Approximation Algorithms
-
--   Problems involving finding the best subset of a variable to max/min an objective value are generalized as the problem of finding the best *power set*.
-
-    -   There are $2^n$ power sets, which becomes impossible to calculate past $n=100$ (depending on the constants)
-
--   *Approximation algorithms* are judged by how fast they are and how close they are to the optimal solution
-
-    -   Forward/backwards stepwise selection is an approximation algorithm to best subset selection
-
-## N-P Complete Problems
-
--   In the power set problem, we need to brute force all combinations and test them. Such problems are called *N-P Complete*
-
-    -   A lot of smart people think it's not possible to solve these with efficient algorithms
-
--   It's hard to tell if a problem is N-P complete
-
-    -   Finding the shortest path between two points is N-P complete (travelling salesman)
-
-    -   But the knapsack problem isn't N-P complete because we can solve it using dynamic programming
-
-## Live Coding
-
-You are given an integer array prices where `prices[i]` is the price of a given stock on the $i$th day.On each day, you may decide to buy and/or sell the stock. You can only hold at most one share of the stock at any time. However, you can buy it then immediately sell it on the same day.
-
-Find and return the maximum profit you can achieve.
-
-```{python}
-#| eval: false
-# INPUT
-prices = [7,1,5,3,6,4]
-# OUTPUT
-7
-```
-
-From [leetcode](https://leetcode.com/problems/best-time-to-buy-and-sell-stock-ii/description/)
-
-# Faster Implementations versus Faster Algorithms
-
-## Faster Implementations versus Faster Algorithms
-
--   There are two ways we speed up our code
-
-    -   Use a faster algorithm, such as dynamic programming instead of brute force. Algorithms are concerned with the approach to the problem
-
-    -   Use a faster implementation, such as vectorization instead of loops
-
--   It is useful to think about these separately when developing a programming, then combining them to create a super-fast approach!
-
-# Recommended Problems and References
-
-## Recommended Problems and Readings
-
--   Cormen: Chapter 34 on NP-Completeness (highly optional)
-
--   Bhargava: Chapter 8 exercises
-
-    -   8.1 - 8.8
-
--   Vectorize the second code chunk in the Parallelization section
-
--   [Find the longest palindrome from a string](https://leetcode.com/problems/longest-palindrome/) Hint: use a greedy alogrithm
-
--   [Computing Pascal's triangle](https://leetcode.com/problems/pascals-triangle/) Hint: use dynamic programming
-
-## References
-
--   Bhargava, A. Y. (2016). *Grokking algorithms: An illustrated guide for programmers and other curious people.* Manning. Chapter 1.
-
--   Cormen, T. H. (Ed.). (2009). *Introduction to algorithms* (3rd ed). MIT Press. Chapter 1 and 3.
-
--   
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-\title{Why is my code slow?}
-\author{Salaar Liaqat}
-\date{}
-\institute{Data Sciences Institute, UofT}
-
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-
-\begin{frame}{Outline}
-\protect\hypertarget{outline}{}
-\begin{itemize}
-\item
-  Caching, Memoization, and Vectorization
-\item
-  Parallel Computing
-\item
-  Greedy and Exhaustive Algorithms
-\item
-  Faster Implementations versus Faster Algorithms
-\end{itemize}
-\end{frame}
-
-\hypertarget{caching-memoization-and-vectorization}{%
-\section{Caching, Memoization, and
-Vectorization}\label{caching-memoization-and-vectorization}}
-
-\begin{frame}{Caching}
-\protect\hypertarget{caching}{}
-\begin{itemize}
-\item
-  \emph{Caching} refers to storing things for later use
-
-  \begin{itemize}
-  \item
-    Your browser probably does by temporarily downloading page details
-    on your local disk
-  \item
-    Faster, reduces server load
-  \item
-    Other examples include 3D rendering and saving common database
-    queries
-  \end{itemize}
-\item
-  However, caching usually takes space in exchange for faster run times
-\item
-  The \emph{space-time} trade off is a case where an algorithm trades
-  increased space usage for faster runtimes
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Memoization}
-\protect\hypertarget{memoization}{}
-\begin{itemize}
-\item
-  \emph{Memoization} refers to storing results of function calls to use
-  for later
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Specific method of caching
-  \end{itemize}
-\item
-  This is useful for methods with a lot of repeated computations
-\item
-  For instance, in our recursive Fibonacci number function.
-\item
-  \texttt{fib(12)} is called by \texttt{fib(13)}, \texttt{fib(14)} etc.
-
-  \begin{itemize}
-  \tightlist
-  \item
-    And \texttt{fib(3)} is called many many times
-  \end{itemize}
-\item
-  \(F(5) = F(4) + F(3) = F(3) + F(2) + F(2) + F(1)\) Which calculates
-  repeated subproblems
-\end{itemize}
-\end{frame}
-
-\begin{frame}{How Memoization Works}
-\protect\hypertarget{how-memoization-works}{}
-\begin{itemize}
-\item
-  Since we store the results, each function call is only made once,
-  making the time complexity \(O(n)\), much better than \(O(2^n)\)
-  \footnote<.->{From Bhargava chapter 8}
-\item
-  Memoization can also avoid the maximum recursion depth error because
-  the call stack is smaller
-\end{itemize}
-
-\includegraphics[width=4.44792in,height=\textheight]{images/memo.png}
-\end{frame}
-
-\begin{frame}[fragile]{Memoization Python}
-\protect\hypertarget{memoization-python}{}
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-
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-\end{frame}
-
-\begin{frame}[fragile]{Memoization Python}
-\protect\hypertarget{memoization-python-1}{}
-\begin{itemize}
-\item
-  We can use the \texttt{functools} library, which is included in the
-  standard library (no pip install needed!)
-
-  \begin{itemize}
-  \tightlist
-  \item
-    \texttt{functools} does memoization for you!
-  \end{itemize}
-\item
-  We can use the \texttt{@cache} decorator, but the cached dictionary
-  can grow to massive sizes
-\item
-  Instead, \texttt{@lru\_cache(maxsize\ =\ n)} uses the LRU (least
-  recently used) \texttt{n} computations
-\item
-  Alternatively, we can use \texttt{joblib} to store the memoized
-  results in a file
-\end{itemize}
-\end{frame}
-
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-\end{Highlighting}
-\end{Shaded}
-\end{frame}
-
-\begin{frame}{Vectorized Operations}
-\protect\hypertarget{vectorized-operations}{}
-\begin{itemize}
-\item
-  \emph{Vectorization} is a technique of implementing array operations
-  without for loops
-\item
-  We use functions defined by various modules that are highly optimized
-  for the specific problem
-\item
-  NumPy provides a lot of functions that vectorized and are faster than
-  for loops
-
-  \begin{itemize}
-  \item
-    Array add/subtract/multiply/divide by scalar
-  \item
-    Sum of array
-  \item
-    Max/min of array
-  \end{itemize}
-\item
-  Keep this in mind for some ML processes that are iterative, such as
-  gradient descent
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Why Vectorized Operations Work}
-\protect\hypertarget{why-vectorized-operations-work}{}
-\begin{itemize}
-\item
-  Python (and R) are interpreted languages. There is no compiler and the
-  languages are dynamic
-\item
-  C language, for instance, makes optimization at the compiler level
-  (before execution) to speed up your code
-\item
-  Thus, NumPy implements arrays in C, which speeds things up
-\item
-  The other reason vectorization works in because of parallelization
-\end{itemize}
-\end{frame}
-
-\hypertarget{parallel-computing}{%
-\section{Parallel Computing}\label{parallel-computing}}
-
-\begin{frame}[fragile]{Parallelization}
-\protect\hypertarget{parallelization}{}
-Compare the following codes. What are their run times?
-
-\begin{Shaded}
-\begin{Highlighting}[]
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-
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-\protect\hypertarget{parallelization-1}{}
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-
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-\end{frame}
-
-\begin{frame}{Parallelization}
-\protect\hypertarget{parallelization-2}{}
-\begin{itemize}
-\item
-  Both are \(O(n)\), but the second code chunk can be done in
-  \emph{parallel} because the \(n\) computations are independent.
-\item
-  Fibonacci depends on the previous two values
-\item
-  The requirements for code to the parallelized and vectorized are
-  similar, but not the same
-\item
-  The Numba library can help will parallelizing your code
-\item
-  Note parallel means the process takes place on one machine, but
-  \emph{distributed} means the computation is shared across many
-  machines
-\end{itemize}
-\end{frame}
-
-\hypertarget{greedy-and-exhaustive-algorithms}{%
-\section{Greedy and Exhaustive
-Algorithms}\label{greedy-and-exhaustive-algorithms}}
-
-\begin{frame}{Greedy Approach (literally)}
-\protect\hypertarget{greedy-approach-literally}{}
-\begin{itemize}
-\item
-  Let's revisit the knapsack problem, taking a different approach.
-\item
-  The items are:
-
-  \begin{itemize}
-  \item
-    Stereo: \$3000, 4 kg
-  \item
-    Laptop: \$2000, 3 kg
-  \item
-    Guitar: \$1500, 1 kg
-  \end{itemize}
-\item
-  If we follow the rule ``get the most valuable item, then get second
-  most valuable etc.'' we would make \$3000 by taking the stereo, which
-  isn't the optimal \$3500
-\item
-  A \emph{greedy algorithm} picks the optimal move at each step, which
-  hopefully leads to the overall optimal solution
-
-  \begin{itemize}
-  \tightlist
-  \item
-    But it finds the solution in \(O(n)\) time
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Greedy Apporach}
-\protect\hypertarget{greedy-apporach}{}
-\begin{itemize}
-\item
-  Let's say you could take fractions of an item and we tried the greedy
-  approach
-
-  \begin{itemize}
-  \item
-    Peanuts: \$7/kg
-  \item
-    Rice: \$5/kg
-  \item
-    Tea: \$12/kg
-  \end{itemize}
-\item
-  We would take tea until it runs out, followed by peanuts and rice.
-  This is the optimal solution in \(O(n)\) time!
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Classroom Scheduling Problem}
-\protect\hypertarget{classroom-scheduling-problem}{}
-\begin{itemize}
-\tightlist
-\item
-  Suppose we want to hold as many classes in a classroom as possible
-  \footnote<.->{From Bhargava chapter 8}
-\end{itemize}
-
-\begin{longtable}[]{@{}lll@{}}
-\toprule\noalign{}
-Class & Start & End \\
-\midrule\noalign{}
-\endhead
-Yoga & 9AM & 10AM \\
-Music Theory & 9:30AM & 11AM \\
-Painting & 10AM & 11AM \\
-Algorithms & 10:30AM & 11:30AM \\
-Calculus & 11AM & 12PM \\
-\bottomrule\noalign{}
-\end{longtable}
-
-2 Minutes: write down a greedy algorithm to solve this problem
-\end{frame}
-
-\begin{frame}{Classroom Scheduling Problem}
-\protect\hypertarget{classroom-scheduling-problem-1}{}
-Algorithm
-
-\begin{enumerate}
-\item
-  Pick the class that ends the soonest. This is the first class you'll
-  hold in this classroom
-\item
-  Now, you have to pick a class that starts after the first class.
-  Again, pick the class that ends the soonest. This is the second class
-  you'll hold
-\item
-  Repeat the second step
-\end{enumerate}
-
-This not only produces the correct solution but also does so in \(O(n)\)
-time, for \(n\) classes!
-\end{frame}
-
-\begin{frame}{Classroom Scheduling Problem}
-\protect\hypertarget{classroom-scheduling-problem-2}{}
-\begin{itemize}
-\item
-  An alternative algorithm is the \emph{exhaustive approach}
-
-  \begin{itemize}
-  \item
-    We try every combination of classes. At the end, we see which
-    solution fits the most classes
-  \item
-    We try every combination of items to steal. At the end, we see which
-    solution has the most value
-  \end{itemize}
-\item
-  While brute forcing might sound always unnecessary, there are cases
-  where it is needed to get the optimal solution
-
-  \begin{itemize}
-  \tightlist
-  \item
-    When performing subset selection for regression or decision tree, we
-    can't guarantee the variables are uncorrelated. So forward/backward
-    stepwise selection isn't guaranteed to produce the best outcome
-  \item
-    More on this in a few slides
-  \end{itemize}
-\item
-  2 minutes: what is the time complexity of best subset selection?
-\end{itemize}
-\end{frame}
-
-\begin{frame}{Greedy Approximation Algorithms}
-\protect\hypertarget{greedy-approximation-algorithms}{}
-\begin{itemize}
-\item
-  Problems involving finding the best subset of a variable to max/min an
-  objective value are generalized as the problem of finding the best
-  \emph{power set}.
-
-  \begin{itemize}
-  \tightlist
-  \item
-    There are \(2^n\) power sets, which becomes impossible to calculate
-    past \(n=100\) (depending on the constants)
-  \end{itemize}
-\item
-  \emph{Approximation algorithms} are judged by how fast they are and
-  how close they are to the optimal solution
-
-  \begin{itemize}
-  \tightlist
-  \item
-    Forward/backwards stepwise selection is an approximation algorithm
-    to best subset selection
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}{N-P Complete Problems}
-\protect\hypertarget{n-p-complete-problems}{}
-\begin{itemize}
-\item
-  In the power set problem, we need to brute force all combinations and
-  test them. Such problems are called \emph{N-P Complete}
-
-  \begin{itemize}
-  \tightlist
-  \item
-    A lot of smart people think it's not possible to solve these with
-    efficient algorithms
-  \end{itemize}
-\item
-  It's hard to tell if a problem is N-P complete
-
-  \begin{itemize}
-  \item
-    Finding the shortest path between two points is N-P complete
-    (travelling salesman)
-  \item
-    But the knapsack problem isn't N-P complete because we can solve it
-    using dynamic programming
-  \end{itemize}
-\end{itemize}
-\end{frame}
-
-\begin{frame}[fragile]{Live Coding}
-\protect\hypertarget{live-coding}{}
-You are given an integer array prices where \texttt{prices{[}i{]}} is
-the price of a given stock on the \(i\)th day.On each day, you may
-decide to buy and/or sell the stock. You can only hold at most one share
-of the stock at any time. However, you can buy it then immediately sell
-it on the same day.
-
-Find and return the maximum profit you can achieve.
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\CommentTok{\# INPUT}
-\NormalTok{prices }\OperatorTok{=}\NormalTok{ [}\DecValTok{7}\NormalTok{,}\DecValTok{1}\NormalTok{,}\DecValTok{5}\NormalTok{,}\DecValTok{3}\NormalTok{,}\DecValTok{6}\NormalTok{,}\DecValTok{4}\NormalTok{]}
-\CommentTok{\# OUTPUT}
-\DecValTok{7}
-\end{Highlighting}
-\end{Shaded}
-
-From
-\href{https://leetcode.com/problems/best-time-to-buy-and-sell-stock-ii/description/}{leetcode}
-\end{frame}
-
-\hypertarget{faster-implementations-versus-faster-algorithms}{%
-\section{Faster Implementations versus Faster
-Algorithms}\label{faster-implementations-versus-faster-algorithms}}
-
-\begin{frame}{Faster Implementations versus Faster Algorithms}
-\protect\hypertarget{faster-implementations-versus-faster-algorithms-1}{}
-\begin{itemize}
-\item
-  There are two ways we speed up our code
-
-  \begin{itemize}
-  \item
-    Use a faster algorithm, such as dynamic programming instead of brute
-    force. Algorithms are concerned with the approach to the problem
-  \item
-    Use a faster implementation, such as vectorization instead of loops
-  \end{itemize}
-\item
-  It is useful to think about these separately when developing a
-  programming, then combining them to create a super-fast approach!
-\end{itemize}
-\end{frame}
-
-\hypertarget{recommended-problems-and-references}{%
-\section{Recommended Problems and
-References}\label{recommended-problems-and-references}}
-
-\begin{frame}{Recommended Problems and Readings}
-\protect\hypertarget{recommended-problems-and-readings}{}
-\begin{itemize}
-\item
-  Cormen: Chapter 34 on NP-Completeness (highly optional)
-\item
-  Bhargava: Chapter 8 exercises
-
-  \begin{itemize}
-  \tightlist
-  \item
-    8.1 - 8.8
-  \end{itemize}
-\item
-  Vectorize the second code chunk in the Parallelization section
-\item
-  \href{https://leetcode.com/problems/longest-palindrome/}{Find the
-  longest palindrome from a string} Hint: use a greedy alogrithm
-\item
-  \href{https://leetcode.com/problems/pascals-triangle/}{Computing
-  Pascal's triangle} Hint: use dynamic programming
-\end{itemize}
-\end{frame}
-
-\begin{frame}{References}
-\protect\hypertarget{references}{}
-\begin{itemize}
-\item
-  Bhargava, A. Y. (2016). \emph{Grokking algorithms: An illustrated
-  guide for programmers and other curious people.} Manning. Chapter 1.
-\item
-  Cormen, T. H. (Ed.). (2009). \emph{Introduction to algorithms} (3rd
-  ed). MIT Press. Chapter 1 and 3.
-\item
-\end{itemize}
-\end{frame}
-
-
-
-\end{document}
diff --git a/slides-resources/6_slow-code/6_slow-code.toc b/slides-resources/6_slow-code/6_slow-code.toc
deleted file mode 100644
index 830bc5a..0000000
--- a/slides-resources/6_slow-code/6_slow-code.toc
+++ /dev/null
@@ -1,5 +0,0 @@
-\beamer@sectionintoc {1}{Caching, Memoization, and Vectorization}{3}{0}{1}
-\beamer@sectionintoc {2}{Parallel Computing}{12}{0}{2}
-\beamer@sectionintoc {3}{Greedy and Exhaustive Algorithms}{16}{0}{3}
-\beamer@sectionintoc {4}{Faster Implementations versus Faster Algorithms}{25}{0}{4}
-\beamer@sectionintoc {5}{Recommended Problems and References}{27}{0}{5}
diff --git a/slides-resources/6_slow-code/6_slow-code.vrb b/slides-resources/6_slow-code/6_slow-code.vrb
deleted file mode 100644
index cec6085..0000000
--- a/slides-resources/6_slow-code/6_slow-code.vrb
+++ /dev/null
@@ -1,21 +0,0 @@
-\frametitle{Live Coding}
-\protect\hypertarget{live-coding}{}
-You are given an integer array prices where \texttt{prices{[}i{]}} is
-the price of a given stock on the \(i\)th day.On each day, you may
-decide to buy and/or sell the stock. You can only hold at most one share
-of the stock at any time. However, you can buy it then immediately sell
-it on the same day.
-
-Find and return the maximum profit you can achieve.
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\CommentTok{\# INPUT}
-\NormalTok{prices }\OperatorTok{=}\NormalTok{ [}\DecValTok{7}\NormalTok{,}\DecValTok{1}\NormalTok{,}\DecValTok{5}\NormalTok{,}\DecValTok{3}\NormalTok{,}\DecValTok{6}\NormalTok{,}\DecValTok{4}\NormalTok{]}
-\CommentTok{\# OUTPUT}
-\DecValTok{7}
-\end{Highlighting}
-\end{Shaded}
-
-From
-\href{https://leetcode.com/problems/best-time-to-buy-and-sell-stock-ii/description/}{leetcode}
diff --git a/steps_to_ask_for_help.png b/steps_to_ask_for_help.png
new file mode 100644
index 0000000..edc07a2
Binary files /dev/null and b/steps_to_ask_for_help.png differ