big update: rework all notebooks

Author: jakevdp

.gitignore

@@ -56,4 +56,9 @@ docs/_build/
 # PyBuilder
 target/
 
-.ipynb_checkpoints
+# IPython
+.ipynb_checkpoints
+notebooks/.ipynb_checkpoints
+
+# Emacs
+*~

README.md

@@ -18,13 +18,14 @@ This tutorial requires the following packages:
 - `matplotlib` version 1.3 or later: http://matplotlib.org/
 - `scikit-learn` version 0.14 or later: http://scikit-learn.org
 - `ipython` version 2.0 or later, with notebook support: http://ipython.org
+- `seaborn` version 0.5 or later
 
 The easiest way to get these is to use the [conda](https://store.continuum.io/) environment manager.
 I suggest downloading and installing [miniconda](conda.pydata.org/miniconda.html).
 
 Once this is installed, the following command will install all required packages in your Python environment:
 ```
-$ conda install numpy scipy matplotlib scikit-learn ipython-notebook
+$ conda install numpy scipy matplotlib scikit-learn ipython-notebook seaborn
 ```
 
 Alternatively, you can download and install the (very large) Anaconda software distribution, found at https://store.continuum.io/.

notebooks/.ipynb_checkpoints/01_basics-checkpoint.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:de241aaf4c4e358ccdf08ae1d006c41163b68f28cbe4c590a72cc90148201d1c"
+  "signature": "sha256:152718c240149636149bc1ec5b47f9a1b11033c64ec3350769144260c578a94a"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -53,12 +53,11 @@
      "source": [
       "Outline:\n",
       "\n",
-      "**9:00 - 9:30** Preliminaries: Setup & introduction\n",
+      "**9:00 - 9:15** Preliminaries: Setup & introduction\n",
       "* Making sure your computer is set-up\n",
-      "* What is Machine Learning?\n",
-      "* Quick review of Numpy and Matplotlib\n",
       "\n",
-      "**9:30 - 10:15** Basic Principles of Machine Learning and the Scikit-learn Interface\n",
+      "**9:15 - 10:00** Basic Principles of Machine Learning and the Scikit-learn Interface\n",
+      "* What is Machine Learning?\n",
       "* Machine learning data layout\n",
       "* Supervised Learning\n",
       "    - Classification\n",
@@ -67,15 +66,20 @@
       "* Unsupervised Learning\n",
       "    - Clustering\n",
       "    - Dimensionality Reduction\n",
-      "* Evaluation of models\n",
-      "* How to choose the right algorithm for your dataset\n",
+      "    - Density Estimation\n",
+      "* Evaluation of Learning Models\n",
+      "* Choosing the right algorithm for your dataset\n",
+      "\n",
+      "**10:00 - 10:45** Supervised learning in-depth\n",
+      "* Support Vector Machines\n",
+      "* Decision Trees and Random Forests\n",
       "\n",
-      "**10:15 - 11:00** Supervised learning in-depth\n",
-      "* Two important algorithms: Support Vector Machines and Random Forests\n",
-      "* Application: recognizing handwritten digits\n",
+      "**10:45 - 11:00**: break\n",
       "\n",
       "**11:00 - 11:45** Unsupervised learning in-depth\n",
-      "* Two important algorithms: PCA and K Means\n",
+      "* Dimensionality Reduction: Principal Component Analysis\n",
+      "* Clustering: K Means\n",
+      "* Density Estimation: Gaussian Mixture Models\n",
       "* Application: image color compression\n",
       "\n",
       "**11:45 - 12:20** Validation and Model Selection\n",
@@ -104,6 +108,7 @@
       "- `matplotlib` version 1.3 or later: http://matplotlib.org/\n",
       "- `scikit-learn` version 0.14 or later: http://scikit-learn.org\n",
       "- `ipython` version 2.0 or later, with notebook support: http://ipython.org\n",
+      "- `seaborn`: version 0.5 or later, used mainly for plot styling\n",
       "\n",
       "The easiest way to get these is to use the [conda](https://store.continuum.io/) environment manager.\n",
       "I suggest downloading and installing [miniconda](conda.pydata.org/miniconda.html).\n",
@@ -146,38 +151,14 @@
       "print('matplotlib:', matplotlib.__version__)\n",
       "\n",
       "import sklearn\n",
-      "print('scikit-learn:', sklearn.__version__)"
+      "print('scikit-learn:', sklearn.__version__)\n",
+      "\n",
+      "import seaborn\n",
+      "print('seaborn', seaborn.__version__)"
      ],
      "language": "python",
      "metadata": {},
-     "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        "IPython: 2.4.1\n",
-        "numpy:"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        " 1.9.2\n",
-        "scipy: 0.15.1\n",
-        "matplotlib: 1.4.3\n",
-        "scikit-learn:"
-       ]
-      },
-      {
-       "output_type": "stream",
-       "stream": "stdout",
-       "text": [
-        " 0.15.2\n"
-       ]
-      }
-     ],
-     "prompt_number": 1
+     "outputs": []
     },
     {
      "cell_type": "heading",