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<!DOCTYPE html>
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<title>CS229: Machine Learning</title>
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<li class="nav-item"><a class="nav-link" href="./index.html#announcement">Announcements</a></li>
<li class="nav-item"><a class="nav-link" href="./syllabus.html">Syllabus</a></li>
<li class="nav-item"><a class="nav-link" href="./index.html#info">Course Info</a></li>
<li class="nav-item"><a class="nav-link" href="./index.html#logistics">Logistics</a></li>
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<h2>Syllabus and Course Schedule</h2>
<p>
[Previous offerings: <a href="syllabus-autumn2018.html"> Autumn 2018</a>, <a href="syllabus-spring2019.html">Spring 2019</a>] </p>
<br>
</div>
</div>
<div class="container">
<strong>*</strong> Below is a collection of topics, of which we plan to cover a large subset this quarter. The specific topics and the order is subject to change.
<table id="topics" class="table table-bordered no-more-tables">
<thead class="active" style="background-color:#f9f9f9">
<th>Category</th><th>Topic</th>
</thead>
<tbody>
<!--<tr>
<td colspan="4" style="text-align:center; vertical-align:middle;background-color:#fffde7">
<strong>Introduction</strong> (1 class)
</td>
</tr>-->
<tr>
<td>Review</td> <td>
<ul>
<li> Linear Algebra
<li> Matrix Calculus
<li> Probability and Statistics
</ul>
</td>
</tr>
<tr>
<td>Supervised Learning</td>
<td> <ul>
<li> Linear Regression (Gradient Descent, Normal Equations)
<li> Weighted Linear Regression (LWR)
<li> Logistic Regression, Perceptron
<li> Newton's Method, KL-divergence, (cross-)Entropy, Natural Gradient
<li> Exponential Family and Generalized Linear Models
<li> Generative Models (Gaussian Discriminant Analysis, Naive Bayes)
<li> Kernel Method (SVM, Gaussian Processes)
<li> Tree Ensembles (Decision trees, Random Forests, Boosting and Gradient Boosting)
</ul>
</td>
</tr>
<tr>
<td> Learning Theory </td>
<td> <ul>
<li> Regularization
<li> Bias-Variance Decomposition and Tradeoff
<li> Concentration Inequalities
<li> Generalization and Uniform Convergence
<li> VC-dimension
</ul>
</td>
</tr>
<tr>
<td> Deep Learning </td>
<td> <ul> <li> Neural Networks <li> Backpropagation <li> Deep Architectures </ul> </td>
</tr>
<tr>
<td> Unsupervised Learning </td>
<td> <ul>
<li> K-means
<li> Gaussian Mixture Model (GMM)
<li> Expectation Maximization (EM)
<li> Variational Auto-encoder (VAE)
<li> Factor Analysis
<li> Principal Components Analysis (PCA)
<li> Independent Components Analysis (ICA)
</ul> </td>
</tr>
<tr>
<td> Reinforcement Learning (RL) </td>
<td>
<ul>
<li> Markov Decision Processes (MDP)
<li> Bellmans Equations
<li> Value Iteration and Policy Iteration
<li> Value Function Approximation
<li> Q-Learning
</ul>
</td>
</tr>
<tr>
<td> Application </td>
<td>
<ul>
<li> Advice on structuring an ML project
<li> Evaluation Metrics
</ul>
</td>
</table>
<div> </div>
<div class="container">
This table will be updated regularly through the quarter to reflect what was actually covered, along with corresponding readings and notes.
<table id="schedule" class="table table-bordered no-more-tables">
<thead class="active" style="background-color:#f9f9f9">
<th>Event</th><th>Date</th><th>Description</th><th>Materials and Assignments</th>
</thead>
<tbody>
<!--<tr>
<td colspan="4" style="text-align:center; vertical-align:middle;background-color:#fffde7">
<strong>Introduction</strong> (1 class)
</td>
</tr>-->
<tr>
<td>Lecture 1</td>
<td> 6/24 </td>
<td> <ul> <li> Introduction and Logistics </li> <li> Review of Linear Algebra </li>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Introduction [<a href="summer2019/CS229-Intro.pptx">pptx</a>]<br>
<li> Linear Algebra (section 1-3) [<a href="summer2019/cs229-linalg.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 2</td>
<td>6/26</td>
<td>
<ul>
<li> Review of Matrix Calculus
<li> Review of Probability
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Linear Algebra (section 4) [<a href="summer2019/cs229-linalg.pdf">pdf</a>]
<li> Probability Theory [<a href="summer2019/cs229-prob.pdf">pdf</a>]
<li> Probability Theory Slides [<a href="summer2019/cs229-prob-slide.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 3</td>
<td>6/28</td>
<td>
<ul>
<li> Review of Probability and Statistics
<li> Setting of Supervised Learning
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Supervised Learning [<a href="summer2019/cs229-notes1.pdf">pdf</a>]
<li> Probability Theory [<a href="summer2019/cs229-prob.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 4</td>
<td>7/1</td>
<td>
<ul>
<li> Linear Regression
<li> Gradient Descent (GD), Stochastic Gradient Descent (SGD)
<li> Normal Equations
<li> Probabilistic Interpretation
<li> Maximum Likelihood Estimation (MLE)
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Supervised Learning (section 1-3) [<a href="summer2019/cs229-notes1.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 5</td>
<td>7/3</td>
<td>
<ul>
<li> Perceptron
<li> Logistic Regression
<li> Newton's Method
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Supervised Learning (section 5-7) [<a href="summer2019/cs229-notes1.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 6</td>
<td>7/5</td>
<td>
<ul>
<li> Exponential Family
<li> Generalized Linear Models (GLM)
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Supervised Learning (section 8-9) [<a href="summer2019/cs229-notes1.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 7</td>
<td>7/8</td>
<td>
<ul>
<li> Gaussian Discriminant Analysis (GDA)
<li> Naive Bayes
<li> Laplace Smoothing
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Generative Algorithms [<a href="summer2019/cs229-notes2.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 8</td>
<td>7/10</td>
<td>
<ul>
<li> Kernel Methods
<li> Support Vector Machine
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Kernel Methods and SVM [<a href="summer2019/cs229-notes3.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 9</td>
<td>7/12</td>
<td>
<ul> <li> Gaussian Processes </ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li>Gaussian Processes [<a href="summer2019/gaussian_processes.pdf">pdf</a>] </li>
</ul>
<strong>Optional</strong>
<ul>
<li>The Multivariate Gaussian Distribution [<a href="summer2019/gaussians.pdf">pdf</a>] </li>
<li>More on Gaussian Distribution [<a href="summer2019/more_on_gaussians.pdf">pdf</a>] </li>
</ul>
</td>
</tr>
<tr>
<td>Lecture 10</td>
<td>7/15</td>
<td>
<ul> <li> Neural Networks and Deep Learning </ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Deep Learning (skip Sec 3.3) [<a href="summer2019/cs229-notes-deep_learning.pdf">pdf</a>]
</ul>
<strong> Optional </strong>
<ul>
<li> Backpropagation [<a href="notes-spring2019/backprop.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 11</td>
<td>7/17</td>
<td>
<ul>
<li> Deep Learning (contd)
</ul>
</td>
<td>
</td>
</tr>
<tr>
<td>Lecture 12</td>
<td>7/19</td>
<td>
<ul>
<li> Bias and Variance
<li> Regularization, Bayesian Interpretation
<li> Model Selection
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Regularization and Model Selection [<a href="summer2019/cs229-notes5.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 13</td>
<td>7/22</td>
<td>
<ul>
<li> Bias-Variance tradeoff (wrap-up)
<li> Uniform Convergence
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Bias Variance Analysis [<a href="summer2019/BiasVarianceAnalysis.pdf">pdf</a>]
<li> Statistical Learning Theory [<a href="summer2019/cs229-notes4.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 14</td>
<td>7/24</td>
<td>
<ul>
<li> Reinforcement Learning (RL)
<li> Markov Decision Processes (MDP)
<li> Value and Policy Iterations
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Reinforcement Learning and Control (Sec 1-2) [<a href="summer2019/cs229-notes12.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 15</td>
<td>7/26</td>
<td>
<ul>
<li> RL (wrap-up)
<li> Learning MDP model
<li> Continuous States
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Reinforcement Learning and Control (Sec 3-4) [<a href="summer2019/cs229-notes12.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 16</td>
<td>7/29</td>
<td>
Unsupervised Learning
<ul>
<li> K-means clustering
<li> Mixture of Gaussians (GMM)
<li> Expectation Maximization (EM)
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> K-means [<a href="summer2019/cs229-notes7a.pdf">pdf</a>]
<li> Mixture of Gaussians [<a href="summer2019/cs229-notes7b.pdf">pdf</a>]
<li> Expectation Maximization (Sec 1-2, skip 2.1) [<a href="summer2019/cs229-notes8.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 17</td>
<td>7/31</td>
<td>
<ul>
<li> EM (wrap-up)
<li> Factor Analysis
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Expectation Maximization (Sec 3) [<a href="summer2019/cs229-notes8.pdf">pdf</a>]
<li> Factor Analysis [<a href="summer2019/cs229-notes9.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 18</td>
<td>8/2</td>
<td>
<ul>
<li> Factor Analysis (wrap-up)
<li> Principal Components Analysis (PCA)
<li> Independent Components Analysis (ICA)
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Principal Components Analysis [<a href="summer2019/cs229-notes10.pdf">pdf</a>]
<li> Independent Components Analysis [<a href="summer2019/cs229-notes11.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 19</td>
<td>8/5</td>
<td>
<ul>
<li> Maximum Entropy and Exponential Family
<li> KL-Divergence
<li> Calibration and Proper Scoring Rules
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Maximum Entropy [<a href="summer2019/MaxEnt.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 20</td>
<td>8/7</td>
<td>
<ul>
<li> Variational Inference
<li> EM Variants
<li> Variational Autoencoder
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> VAE (Sec 4) [<a href="summer2019/cs229-notes8.pdf">pdf</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 21</td>
<td>8/9</td>
<td>
<ul>
<li> Evaluation Metrics </li>
</ul>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li> Evaluation Metrics [<a href="summer2019/EvaluationMetrics.pptx">pptx</a>]
</ul>
</td>
</tr>
<tr>
<td>Lecture 22</td>
<td>8/12</td>
<td>
<ul>
<li> Practical advice and tips
<li> Review for Finals
</ul>
</td>
<td>
<strong>Class Notes</strong>
</td>
</tr>
<tr>
<td>Lecture 23</td>
<td>8/14</td>
<td>
<ul>
<li> Review for Finals
</ul>
</td>
<td>
<strong>Class Notes</strong>
</td>
</tr>
<tr style="vertical-align:middle;background-color:#FFF2F2">
<td>Final</td>
<td> 8/16 </td>
<td></td>
<td></td>
</tr>
<!-- <tr style="text-align:center; vertical-align:middle;background-color:#FFF2F2">
<td>A0</td>
<td> 4/3 </td>
<td colspan="3" style="text-align:center; vertical-align:middle;">
<strong>Problem Set 0</strong> <a href="https://piazza.com/class/jtuwk7ilolqub?cid=22">[pdf]</a> <a href="https://piazza.com/class/jtuwk7ilolqub?cid=138">[solution]</a>. Out 4/1. Due 4/10. <a href="gradescope.html">Submission instructions</a>.
</td>
</tr> -->
<!-- <tr>
<td>Lecture 3</td>
<td>6/28</td>
<td colspan="2">
<strong>Discussion Section</strong>: Linear Algebra [<a href="http://cs229.stanford.edu/section-spring2019/cs229-linalg.pdf">Notes</a>]<br>
</td>
</tr> -->
<tr>
<td colspan="4">
<b>Other Resources</b>
<ol>
<li>Advice on applying machine learning: Slides from Andrew's lecture on getting machine learning algorithms to work in practice can be found <a href="http://cs229.stanford.edu/materials/ML-advice.pdf">here</a>.<br></li>
<li>Previous projects: A list of last year's final projects can be found <a href="http://cs229.stanford.edu/proj2017/index.html">here</a>.<br></li>
<li>Data: Here is the <a href="http://www.ics.uci.edu/~mlearn/MLRepository.html">UCI Machine learning repository</a>, which contains a large collection of standard datasets for testing learning algorithms. If you want to see examples of recent work in machine learning, start by taking a look at the conferences <a href="http://www.nips.cc/">NeurIPS</a> (all old NeurIPS papers are online) and ICML. Some other related conferences include UAI, AAAI, IJCAI.<br></li>
<li>Viewing PostScript and PDF files: Depending on the computer you are using, you may be able to download a <a href="http://www.cs.wisc.edu/~ghost/">PostScript</a> viewer or <a href="http://www.adobe.com/products/acrobat/readstep2_allversions.html">PDF viewer</a> for it if you don't already have one.<br></li>
<li><a href="https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-supervised-learning">Machine learning study guides tailored to CS 229</a> by Afshine Amidi and Shervine Amidi.</li>
</ol>
</td>
</tr>
</tbody></table>
</div>
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