tutorials week1-5 with Sergi

Author: levon-d

our-initiatives/tutorials copy/_category_.json

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+{
+  "label": "💻 ML Tutorial Series",
+  "position": 2,
+  "link": {
+    "type": "doc",
+    "id": "tutorials/index"
+  }
+}

our-initiatives/tutorials copy/classification.md

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+---
+sidebar_position: 5
+---
+
+# 3: Classification
+
+**Date: 1st November 2023**
+
+💡 **Classification** helps us to group data into predefined classes. **Logistic Regression** is a linear model used for binary classification, while **K-means** is an unsupervised clustering algorithm that categorizes data into clusters. **Support Vector Machines (SVM)** find an optimal hyperplane to separate data, and **Decision Trees** use a tree-like structure to make decisions based on feature attributes. Come to this session to explore and implement these four well-known classification methods! 💡
+
+You can access our **demonstration notebook** here: 📘 [**Tutorial 3 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-4/blob/main/week-3/p1_logistic_regression_exercise.ipynb)
+
+The folder contains notebooks for Logistic Regression, SVMs, Decision Trees and K-Means. The solutions are available in the same folder.
+
+You can access our **slides** here: 💻 [**Tutorial 3 Slides**](https://www.canva.com/design/DAFqI2cJlw0/Y-hfq3RUexRi18DBV0R0VA/edit?utm_content=DAFqI2cJlw0&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton)
+
+The **recording** from this session is available here: 🎤 [**Tutorial 3 Recording**](https://youtu.be/GUxbM4EJrMo?si=jFVr8klkYPsygzSD)
+
+The **DOXA Challenge notebook** can be found here: 🏆 [**DOXA Challenge 1**](https://doxaai.com/competition/uclais-2023-1)
+
+**This is now open for submissions!** Let's see how well your models can perform🙌🏼
+
+This is the end of the **Classical Machine Learning** section of the series. We will continute next half term on **Deep Learning** 🥳
+
+Hope you have a great reading week! Please join our **WhatsApp** group chat through this [link](https://chat.whatsapp.com/JWEJn7OWvWE8MBfm2uSBhh).

our-initiatives/tutorials/climate-hack.md renamed to our-initiatives/tutorials copy/climate-hack.md

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+---
+sidebar_position: 1
+---
+
+import DocCardList from '@theme/DocCardList'
+
+# 💻 Machine Learning Tutorial Series
+
+Welcome to season 4 (2024-25) of the beginner machine learning tutorial series of the UCL Artificial Intelligence Society!
+
+If you have any questions about our content or machine learning more generally, feel free to ask us at the next session or make a forum post on the [UCLAIS Discord server](https://discord.gg/KSUZuQx?ltclid=3f704b3b-9044-415a-a2d7-e41007214187). You can also join our WhatsApp group chat through this [link](https://chat.whatsapp.com/JWEJn7OWvWE8MBfm2uSBhh).
+
+## Our Team
+
+This academic year, the tutorial series is being delivered by the following people:
+
+- [Sergi Kavtaradze](#) (Head of Tutorials)
+- [Zachary Baker](#) (ML Officer)
+- [Paul Chaminieu](#) (ML Officer)
+- [Anna-Maria](#) (ML Officer)
+
+## DOXA Challenges
+
+Our teaching will be supplemented by engaging AI competitions on [DOXA](https://doxaai.com/) related to topics such as visual computing, natural language processing and reinforcement learning.
+
+To take part and follow along with the tutorial series content, [sign up](https://doxaai.com/sign-up) to the platform if you have not done so already.
+
+## Weekly Tutorials
+
+📚 Access our notebooks, slides and recordings here!
+
+<DocCardList />
+
+## Timeline
+
+### Term 1
+
+During the first half term, we aim to cover basic concepts of **classical ML**:
+
+- Tutorial 0: **Introduction to AI**
+- Tutorial 1: **Introduction to Python**
+- Tutorial 2: **Regression**
+- Tutorial 3: **Classification 1**
+- Tutorial 4: **Classification 2** 
+
+After reading week, we will focus on **Deep Learning**!
+
+- Tutorial 4: **Neural Networks**
+- Tutorial 5: **Visual Computing I** (Doxa)
+- Tutorial 6: **Generative visual computing**
+- Tutorial 7: **Recurrent Neural Networks** (Doxa)
+- Tutorial 8: **Introduction to Transforments**
+
+### Term 2
+
+- Tutorial 9: **Natural Language Processing I**
+- Tutorial 10: **Natural Language Processing II**
+- Tutorial 11: **Graph neural networks / Reinforcement learning**
+
+## Previous Seasons
+
+The content and resources from previous years are available on GitHub:
+
+- [Season 1 (2020/21)](https://github.com/UCLAIS/Machine-Learning-Tutorials) &ndash; led by [Danny Toeun Kim](https://github.com/kimdanny)
+- [Season 2 (2021/22)](https://github.com/UCLAIS/ML-Tutorials-Season-2) &ndash; led by [Martynas Pocius](https://github.com/MartynasPocius)
+- [Season 3 (2022/23)](https://github.com/UCLAIS/ml-tutorials-season-3) &ndash; led by [Filip Trhlík](https://trhlikfilip.com/)
+- [Season 4 (2023/24)](https://github.com/UCLAIS/ml-tutorials-season-4) &ndash; led by [Angela Yu](https://github.com/angela24680403)

our-initiatives/tutorials copy/index.mdx

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+---
+sidebar_position: 2
+---
+
+# 0: Introduction to AI
+
+**Date: 2nd October 2024**
+
+💡 We're excited to present our **Introduction to Artificial Intelligence** tutorial session! In this session, we'll take you on a journey to understand the different perspectives on AI and its evolution. We will also cover definitions of the key components of AI, including **Machine Learning** (ML) and **Deep Learning** (DL). **Generative AI**, especially models like GPT-3 and updated versions of this, has created significant attention in recent years. These models have demonstrated the ability to **generate human-like text and creative content** across multiple areas, ranging from **text to image and audio synthesis**. We will showcase some of these examples in the session! Please note that though this session is introducing what AI is in general, we will be mainly **focusing our series on ML**. 💡
+
+You can access our **demonstration notebook** here: 📘 [**Tutorial 0 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-5/blob/main/week-0/ai_code_examples.ipynb)
+
+You can access our **slides** here: 💻 [**Tutorial 0 Slides**](https://www.canva.com/design/DAFm9tHNEDM/cEKEXWzmazR5KKN-7f06tw/edit)
+
+The **recording** from this session is available here: 🎤 [**Tutorial 0 Recording**](https://www.youtube.com/watch?v=OFS90-FX6pg)

our-initiatives/tutorials copy/intro-to-ai.md

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+---
+sidebar_position: 3
+---
+
+# 1: Introduction to Python
+
+**Date: 9th October 2024**
+
+💡 **Python** is a common, **high-level programming language** known for its simplicity - this means that if you are new to coding, this language would be the perfect place to start! It has access to a lot of **libraries and frameworks**, including **NumPy**, **Pandas**, and **TensorFlow**, that are particularly useful for our ML series. In this session, the Tutorials team is collaborating with the Development team to create a workshop where we can provide direct help with **setting up your environment** and **walk you through Python exercises** we have prepared for you to get started! 💡
+
+You can access our **demonstration notebook** here: 📘 [**Tutorial 1 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-5/blob/main/week-1/1_1_introduction_to_python.ipynb)
+
+Here are the **exercises** you can go through: 📘 [**Tutorial 1 Exercises**](https://github.com/UCLAIS/ml-tutorials-season-5/blob/main/week-1/1_2_python_exercises.ipynb)
+
+You can access our **slides** here: 💻 [**Tutorial 1 Slides**](https://www.canva.com/design/DAFmvE-ptx0/lyY0SiOcjgSxrb201KcC8w/edit)
+
+The **recording** from this session is available here: 🎤 [**Tutorial 1 Recording**](https://www.youtube.com/watch?v=adnK-hc2DmY)
+
+Here are two videos covering our exercise **solutions**:
+
+- Exercises 1 to 4: [Solution pt1](https://www.youtube.com/watch?v=qmHgg0IR680)
+- Exercises 5 and 6: [Solution pt2](https://www.youtube.com/watch?v=3Kmt0qmibrg)
+
+We will not go through all of the notebooks in the session, but throughout this series, we will be referring back to them.
+
+For more information on various **Python libraries**, we have **extra resources** available here: 📘 [**Python Notebook Collection**](https://github.com/UCLAIS/ml-tutorials-season-4/tree/main/week-1)

our-initiatives/tutorials copy/intro-to-python.md

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+---
+sidebar_position: 4
+---
+
+# 2: Regression
+
+**Date: 25th October 2023**
+
+💡 **Linear regression** in simple terms is finding the **line of best fit** given a set of data points that loosely follow a linear pattern. One way of doing this is by **minimising the loss** (difference between actual and computed value), making it useful for **predicting numerical outcomes** based on input features. **Polynomial regression** extends linear regression by allowing the model to capture **nonlinear relationships** between variables, using polynomial functions to fit the data more flexibly. 💡
+
+You can access our **demonstration notebook** here: 📘 [**Tutorial 2 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-4/blob/main/week-2/linear_regression_exercise.ipynb)
+
+The solution is available in the same folder.
+
+You can access our **slides** here: 💻 [**Tutorial 2 Slides**](https://www.canva.com/design/DAFoWFiqTgg/9h0jgvGO1Pd35iYhQfFPMQ/edit?utm_content=DAFoWFiqTgg&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton)
+
+The **recording** from this session is available here: 🎤 [**Tutorial 2 Recording**](https://www.youtube.com/watch?v=bo-lbJ--F3g)

our-initiatives/tutorials/ml-career.md renamed to our-initiatives/tutorials copy/ml-career.md

@@ -4,22 +4,14 @@ sidebar_position: 5
 
 # 3: Classification
 
-**Date: 1st November 2023**
+**Date: 23rd of October 2024**
 
-💡 **Classification** helps us to group data into predefined classes. **Logistic Regression** is a linear model used for binary classification, while **K-means** is an unsupervised clustering algorithm that categorizes data into clusters. **Support Vector Machines (SVM)** find an optimal hyperplane to separate data, and **Decision Trees** use a tree-like structure to make decisions based on feature attributes. Come to this session to explore and implement these four well-known classification methods! 💡
+💡 **Classification** helps us to group data into predefined classes. **Logistic Regression** is a linear model used for binary classification, while **K-means** is an unsupervised clustering algorithm that categorizes data into clusters. **Support Vector Machines (SVM)** find an optimal hyperplane to separate data to make decisions based on feature attributes. Come to this session to explore and implement these three well-known classification methods! 💡
 
-You can access our **demonstration notebook** here: 📘 [**Tutorial 3 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-4/blob/main/week-3/p1_logistic_regression_exercise.ipynb)
+You can access our **demonstration notebook** here: 📘 [**Tutorial 3 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-5/tree/main/week-3)
 
-The folder contains notebooks for Logistic Regression, SVMs, Decision Trees and K-Means. The solutions are available in the same folder.
+The folder contains notebooks for Logistic Regression, SVMs and K-Means. The solutions are available in the same folder.
 
-You can access our **slides** here: 💻 [**Tutorial 3 Slides**](https://www.canva.com/design/DAFqI2cJlw0/Y-hfq3RUexRi18DBV0R0VA/edit?utm_content=DAFqI2cJlw0&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton)
+You can access our **slides** here: 💻 [**Tutorial 3 Slides**](https://www.canva.com/design/DAGSEMcVTo0/tg4iuVLCGYIAIrx3lmvzgw/view?utm_content=DAGSEMcVTo0&utm_campaign=designshare&utm_medium=link&utm_source=editor)
 
-The **recording** from this session is available here: 🎤 [**Tutorial 3 Recording**](https://youtu.be/GUxbM4EJrMo?si=jFVr8klkYPsygzSD)
-
-The **DOXA Challenge notebook** can be found here: 🏆 [**DOXA Challenge 1**](https://doxaai.com/competition/uclais-2023-1)
-
-**This is now open for submissions!** Let's see how well your models can perform🙌🏼
-
-This is the end of the **Classical Machine Learning** section of the series. We will continute next half term on **Deep Learning** 🥳
-
-Hope you have a great reading week! Please join our **WhatsApp** group chat through this [link](https://chat.whatsapp.com/JWEJn7OWvWE8MBfm2uSBhh).
+The **recording** from this session is available here: 🎤 [**Tutorial 3 Recording**](https://youtu.be/G1hO7jkDHDg)

our-initiatives/tutorials/neural-networks.md renamed to our-initiatives/tutorials copy/neural-networks.md

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+---
+sidebar_position: 6
+---
+
+# 4: Classification II & Workshops
+
+**Date: 30th of October 2024**
+
+💡 **Classification** helps us to group data into predefined classes. We will recap classification techniques from last week, including **Logistic Regression**, **K-means**, and **Support Vector Machines (SVM)**. We will cover a new technique called **Decision Trees** which uses a tree-like structure to make decisions based on feature attributes. We will introduce 2 practical machine learning challenges: 
+1. [🐧 Penguin species classification](https://doxaai.com/competition/palmer-penguins)
+2. [🏠 California housing price prediction](https://doxaai.com/competition/california-housing/overview)
+
+
+Over the next 3 weeks until 13 Nov, 2024 18:00 we will be running the housing price prediction DOXA challenge
+
+
+People who manage to get MAE of under 32.6 will be eligible for **AI society shirt** and a pen (up to 2 or 3 shirts available, so first come first serve).
+
+Check out the last part of our presentation discussing methods to get a better MAE (mean absolute error). We will teach you about ensemble methods including **gradient boosting** and **feature scaling** 
+
+You can access our **demonstration notebook** here: 📘 [**Tutorial 4 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-5/tree/main/week-4)
+
+The folder contains notebooks for Decision Trees as well as the DOXA Challenges.
+
+You can access our **slides** here: 💻 [**Tutorial 4 Slides**](https://www.canva.com/design/DAGSEOQht5I/XHpWzq_jCjyy90w2t9XKMQ/view?utm_content=DAGSEOQht5I&utm_campaign=designshare&utm_medium=link&utm_source=editor)
+
+The **recording** from this session is available here: 🎤 [**Tutorial 4 Recording**](https://www.youtube.com/watch?v=0JmuBxJ5W4I)

our-initiatives/tutorials/nlp-1.md renamed to our-initiatives/tutorials copy/nlp-1.md

@@ -40,22 +40,22 @@ During the first half term, we aim to cover basic concepts of **classical ML**:
 - Tutorial 0: **Introduction to AI**
 - Tutorial 1: **Introduction to Python**
 - Tutorial 2: **Regression**
-- Tutorial 3: **Classification 1**
-- Tutorial 4: **Classification 2** 
+- Tutorial 3: **Classification I**
+- Tutorial 4: **Classification II** 
 
 After reading week, we will focus on **Deep Learning**!
 
-- Tutorial 4: **Neural Networks**
-- Tutorial 5: **Visual Computing I** (Doxa)
-- Tutorial 6: **Generative visual computing**
-- Tutorial 7: **Recurrent Neural Networks** (Doxa)
-- Tutorial 8: **Introduction to Transforments**
+- Tutorial 5: **Neural Networks**
+- Tutorial 6: **Visual Computing I** (Doxa)
+- Tutorial 7: **Generative visual computing**
+- Tutorial 8: **Recurrent Neural Networks** (Doxa)
+- Tutorial 9: **Introduction to Transforments**
 
 ### Term 2
 
-- Tutorial 9: **Natural Language Processing I**
-- Tutorial 10: **Natural Language Processing II**
-- Tutorial 11: **Graph neural networks / Reinforcement learning**
+- Tutorial 10: **Natural Language Processing I**
+- Tutorial 11: **Natural Language Processing II**
+- Tutorial 12: **Graph neural networks / Reinforcement learning**
 
 ## Previous Seasons
 

our-initiatives/tutorials/nlp-2.md renamed to our-initiatives/tutorials copy/nlp-2.md

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 You can access our **demonstration notebook** here: 📘 [**Tutorial 0 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-5/blob/main/week-0/ai_code_examples.ipynb)
 
-You can access our **slides** here: 💻 [**Tutorial 0 Slides**](https://www.canva.com/design/DAFm9tHNEDM/cEKEXWzmazR5KKN-7f06tw/edit)
+You can access our **slides** here: 💻 [**Tutorial 0 Slides**](https://www.canva.com/design/DAGSEETkOlo/6odBSGgHuNmrCqUAUwCbAA/view?utm_content=DAGSEETkOlo&utm_campaign=designshare&utm_medium=link&utm_source=editor)
 
-The **recording** from this session is available here: 🎤 [**Tutorial 0 Recording**](https://www.youtube.com/watch?v=OFS90-FX6pg)
+The **recording** from this session is available here: 🎤 [**Tutorial 0 Recording**](https://youtu.be/3E5aampSpvc)

our-initiatives/tutorials copy/regression.md

@@ -6,21 +6,17 @@ sidebar_position: 3
 
 **Date: 9th October 2024**
 
-💡 **Python** is a common, **high-level programming language** known for its simplicity - this means that if you are new to coding, this language would be the perfect place to start! It has access to a lot of **libraries and frameworks**, including **NumPy**, **Pandas**, and **TensorFlow**, that are particularly useful for our ML series. In this session, the Tutorials team is collaborating with the Development team to create a workshop where we can provide direct help with **setting up your environment** and **walk you through Python exercises** we have prepared for you to get started! 💡
+💡 **Python** is a common, **high-level programming language** known for its simplicity - this means that if you are new to coding, this language would be the perfect place to start! It has access to a lot of **libraries and frameworks**, including **NumPy**, **Pandas**, and **TensorFlow**, that are particularly useful for our ML series which we will cover in the following weeks.
 
 You can access our **demonstration notebook** here: 📘 [**Tutorial 1 Notebook**](https://github.com/UCLAIS/ml-tutorials-season-5/blob/main/week-1/1_1_introduction_to_python.ipynb)
 
-Here are the **exercises** you can go through: 📘 [**Tutorial 1 Exercises**](https://github.com/UCLAIS/ml-tutorials-season-5/blob/main/week-1/1_2_python_exercises.ipynb)
+Here are the **exercises** you can go through: 📘 [**Tutorial 1 Exercises**](https://github.com/UCLAIS/ml-tutorials-season-5/blob/main/week-1/1_3_python_exercises_solutions.ipynb)
 
-You can access our **slides** here: 💻 [**Tutorial 1 Slides**](https://www.canva.com/design/DAFmvE-ptx0/lyY0SiOcjgSxrb201KcC8w/edit)
+You can access our **slides** here: 💻 [**Tutorial 1 Slides**](https://www.canva.com/design/DAGSEB7hhYg/1T_j1UdAU0s8gRzbJJB2yQ/view?utm_content=DAGSEB7hhYg&utm_campaign=designshare&utm_medium=link&utm_source=editor)
 
-The **recording** from this session is available here: 🎤 [**Tutorial 1 Recording**](https://www.youtube.com/watch?v=adnK-hc2DmY)
+The **recording** from this session is available here: 🎤 [**Tutorial 1 Recording**](https://www.youtube.com/watch?v=NjVkzWVrPVI&ab_channel=SergiSavanelli)
 
-Here are two videos covering our exercise **solutions**:
+We were not able to cover everything we wanted to during the session. Here are some resources that cover the key concepts that were missing: 
 
-- Exercises 1 to 4: [Solution pt1](https://www.youtube.com/watch?v=qmHgg0IR680)
-- Exercises 5 and 6: [Solution pt2](https://www.youtube.com/watch?v=3Kmt0qmibrg)
-
-We will not go through all of the notebooks in the session, but throughout this series, we will be referring back to them.
-
-For more information on various **Python libraries**, we have **extra resources** available here: 📘 [**Python Notebook Collection**](https://github.com/UCLAIS/ml-tutorials-season-4/tree/main/week-1)
+[**Object Oriented Programming**](https://www.youtube.com/watch?v=q2SGW2VgwAM)
+[**Comprehensions**](https://www.youtube.com/watch?v=twxE0dEp3qQ&t=417s)