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<!DOCTYPE html>
<html lang="en">
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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>
<li class="nav-item"><a class="nav-link" href="projects.html">Projects</a></li>
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<h2>Syllabus and Course Schedule</h2>
<p>
<b>Time and Location</b>:
Monday, Wednesday 4:30-5:50pm, <a href="https://campus-map.stanford.edu/?srch=bishop%20auditorium">Bishop Auditorium</a><br />
<strong>Class Videos</strong>:
Current quarter's class videos are available <a href="http://scpd.stanford.edu">here</a> for SCPD students and <a href="https://mvideox.stanford.edu/">here</a> for non-SCPD students.</p>
<br>
</div>
</div>
<div class="container">
<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> 9/24 </td>
<td>
Introduction and Basic Concepts
</td>
<td>
</td>
</tr>
<tr style="text-align:center; vertical-align:middle;background-color:#FFF2F2">
<td>A0</td>
<td>9/24</td>
<td colspan="3" style="text-align:center; vertical-align:middle;">
<strong>Problem Set 0</strong> <a href="materials/ps0.pdf">[pdf]</a>
</td>
</tr>
<!--<tr>
<td colspan="4" style="text-align:center; vertical-align:middle;background-color:#fffde7">
<strong>Supervised learning</strong> (6 classes)
</td>
</tr>-->
<tr>
<td>Lecture 2</td>
<td>9/26</td>
<td>Supervised Learning Setup. Linear Regression.
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li>Supervised Learning, Discriminative Algorithms [<a href="notes/cs229-notes1.pdf">pdf</a>] </li>
</ul>
</td>
</tr>
<tr>
<td>Section</td>
<td>9/28</td>
<td colspan="2">
<strong>Discussion Section</strong>: Linear Algebra [<a href="section/cs229-linalg.pdf">Notes</a>]<br>
</td>
</tr>
<tr>
<td>Lecture 3</td>
<td>10/1</td>
<td rowspan="2">
Weighted Least Squares. Logistic Regression. Netwon's Method <br>
Perceptron. Exponential Family. Generalized Linear Models.
</td>
<td rowspan="2">
<strong>Class Notes</strong>
<ul>
<li>Generative Algorithms [<a href="notes/cs229-notes2.pdf">pdf</a>] </li>
</ul>
</td>
</tr>
<tr>
<td>Lecture 4</td>
<td>10/3</td>
</tr>
<tr style="text-align:center; vertical-align:middle;background-color:#FFF2F2">
<td>A1</td>
<td>10/3</td>
<td colspan="3" style="text-align:center; vertical-align:middle;">
<!-- to a folder -->
<strong>Problem Set 1</strong> <a href="problem-sets/ps1/">[directory]</a>
</td>
</tr>
<tr>
<td>Section</td>
<td>10/5</td>
<td colspan="2">
<strong>Discussion Section</strong>: Probability[<a href="section/cs229-prob.pdf">Notes</a>][<a href="section/cs229-prob-slide.pdf">Slides</a>]
</td>
</tr>
<tr>
<td>Lecture 5</td>
<td>10/8</td>
<td>
Gaussian Discriminant Analysis. Naive Bayes.
</td>
<td>
</td>
</tr>
<tr>
<td>Lecture 6</td>
<td>10/10</td>
<td>
Laplace Smoothing. Support Vector Machines. <br>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li>Support Vector Machines [<a href="notes/cs229-notes3.pdf">pdf</a>] </li>
</ul>
</td>
</tr>
<tr>
<td>Section</td>
<td>10/12</td>
<td colspan="2">
<strong>Discussion Section</strong>: Python <a href="https://d1b10bmlvqabco.cloudfront.net/attach/jkbylqx4kcp1h3/jm8g1m67da14eq/jn7zkozyyol7/CS229_Python_Tutorial.pdf">[slides]<a> <!--Vectorization[<a href="section/vec_demo/Vectorization_Section.pdf">Slides</a>][<a href="section/vec_demo/knn.py">kNN</a>][<a href="section/vec_demo/lr.ipynb">Logistic Regression</a>][<a href="section/vec_demo/sr.ipynb">Softmax Regression</a>][<a href="section/vec_demo/images.csv">images</a>][<a href="section/vec_demo/labels.csv">labels</a>]-->
</td>
</tr>
<tr>
<td>Lecture 7</td>
<td>10/15</td>
<td>
Support Vector Machines. Kernels.
</td>
<td> </td>
</tr>
<!-- <tr>
<td>Lecture 2</td>
<td> 9/27 </td>
<td rowspan="6">
<strong>Supervised learning</strong> (5 classes)
<ol>
<li>Supervised learning setup. LMS.</li>
<li>Logistic regression. Perceptron. Exponential family. </li>
<li>Generative learning algorithms. Gaussian discriminant analysis. Naive Bayes. </li>
<li>Support vector machines. </li>
<li>Model selection and feature selection. </li>
<li>Evaluating and debugging learning algorithms. </li>
</ol>
</td>
<td rowspan="6">
<strong>Class Notes</strong>
<ul>
<li>Generative Algorithms [<a href="notes/cs229-notes2.pdf">pdf</a>] </li>
<li>Support Vector Machines [<a href="notes/cs229-notes3.pdf">pdf</a>] </li>
</ul>
<strong>Problem Set 1</strong> <a href="ps/ps1/ps1.pdf">[pdf]</a>. Out 10/4. Due 10/18. <a href="gradescope.html">Submission instructions</a>.<br>
<strong>Discussion Section: Probability</strong> [<a href="section/cs229-prob.pdf">Notes</a>][<a href="section/cs229-prob-slide.pdf">Slides</a>]<br>
<strong>Discussion Section: Vectorization</strong> [<a href="section/vec_demo/Vectorization_Section.pdf">Slides</a>][<a href="section/vec_demo/knn.py">kNN</a>][<a href="section/vec_demo/lr.ipynb">Logistic Regression</a>][<a href="section/vec_demo/sr.ipynb">Softmax Regression</a>]<br>
</td>
</tr>
<tr>
<td>
Section
</td>
<td> 9/29 </td>
</tr>
<tr>
<td>Lecture 3</td>
<td> 10/2 </td>
</tr>
<tr>
<td>Lecture 4</td>
<td> 10/4 </td>
</tr>
<tr>
<td>Lecture 5</td>
<td> 10/9 </td>
</tr>
<tr>
<td>Lecture 6</td>
<td> 10/11 </td>
</tr> -->
<!--<tr>
<td colspan="4" style="text-align:center; vertical-align:middle;background-color:#fffde7">
<strong>Learning theory </strong> (2 classes)
</td>
</tr>-->
<tr>
<td>Lecture 8</td>
<td> 10/17 </td>
<td>
Bias-Variance tradeoff. Regularization and model/feature selection.
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li>Bias/variance tradeoff[<a href="notes/cs229-notes4.pdf">pdf</a>]</li>
<li>Error analysis[<a href="section/error-analysis.pdf">pdf</a>]</li>
<!--<li>Learning Theory [<a href="notes/cs229-notes4.pdf">pdf</a>]</li>-->
<li>Regularization and Model Selection [<a href="notes/cs229-notes5.pdf">pdf</a>] </li>
<li>Advice on applying machine learning[<a href="http://cs229.stanford.edu/materials/ML-advice.pdf">pdf</a>]</li>
</ul>
</td>
</tr>
<tr style="text-align:center; vertical-align:middle;background-color:#FFF2F2">
<td>A2</td>
<td>10/17</td>
<td colspan="3" style="text-align:center; vertical-align:middle;">
<strong>Problem Set 2</strong> <a href="problem-sets/ps2/">[directory]</a>
</td>
</tr>
<tr>
<td>Section</td>
<td>10/19</td>
<td colspan="2">
<strong>Discussion Section</strong>: Learning Theory [<a href="notes/cs229-notes4.pdf">pdf</a>]
<!--<ul>
<li>Convex Optimization Overview, Part I [<a href="section/cs229-cvxopt.pdf">pdf</a>]</li>
<li>Convex Optimization Overview, Part II [<a href="section/cs229-cvxopt2.pdf">pdf</a>] </li>
</ul>-->
</td>
</tr>
<tr style="text-align:center; vertical-align:middle;background-color:#FFF2F2">
<td>Project</td>
<td> 10/19 </td>
<td colspan="2" style="text-align:center; vertical-align:middle;">Project proposal due at <strong>11:59pm</strong>.</td>
</tr>
<tr>
<td>Lecture 9</td>
<td> 10/22 </td>
<td>
Tree Ensembles.
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li>Decision trees [<a href="notes/cs229-notes-dt.pdf">pdf</a>]</li>
<li>Ensembling methods [<a href="notes/cs229-notes-ensemble.pdf">pdf</a>]</li>
</ul>
<!--<strong>Related material</strong>
<ul>
<li>ESL 8.7 (Bagging), ESL 9.2 (Decision Trees), ESL 15 (Random Forest) [<a href="https://web.stanford.edu/~hastie/Papers/ESLII.pdf">pdf</a>]</li>
</ul>-->
</td>
</tr>
<!--<tr>
<td colspan="4" style="text-align:center; vertical-align:middle;background-color:#fffde7">
<strong>Deep Learning</strong> (2 classes)
</td>
</tr>-->
<tr>
<td>Lecture 10</td>
<td>10/24</td>
<td>
Neural Networks: Basics<br>
</td>
<td>
<strong>Class Notes</strong>
<ul>
<li>Online Learning and the Perceptron Algorithm. (optional reading) [<a href="notes/cs229-notes6.pdf">pdf</a>] </li>
<li>Deep learning [<a href="notes/cs229-notes-deep_learning.pdf">pdf</a>] </li>
<li>Backpropagation [<a href="notes/cs229-notes-backprop.pdf">pdf</a>] </li>
</ul>
</td>
</tr>
<tr>
<td>Lecture 11</td>
<td>10/29</td>
<td>Neural Networks: Training
</td>
<td> </td>
</tr>
<tr>
<td>Section</td>
<td>10/26</td>
<td colspan="2">
<strong>Discussion Section</strong>: Evaluation Metrics [<a href="section/evaluation_metrics.pdf">Slides</a>]
</td>
</tr>
<!--<tr>
<td colspan="4" style="text-align:center; vertical-align:middle;background-color:#fffde7">
<strong>Unsupervised learning</strong> (5 classes)
</td>
</tr>-->
<tr>
<td>Lecture 12 </td>
<td>10/31</td>
<td>Practical Advice for ML projects
</td>
<td rowspan="5">
<strong>Class Notes</strong>
<ul>
<li>Unsupervised Learning, k-means clustering. [<a href="notes/cs229-notes7a.pdf">pdf</a>]</li>
<li>Mixture of Gaussians [<a href="notes/cs229-notes7b.pdf">pdf</a>] </li>
<li>The EM Algorithm [<a href="notes/cs229-notes8.pdf">pdf</a>] </li>
<li>Factor Analysis [<a href="notes/cs229-notes9.pdf">pdf</a>]</li>
<li>Principal Components Analysis [<a href="notes/cs229-notes10.pdf">pdf</a>] </li>
<li>Independent Components Analysis [<a href="notes/cs229-notes11.pdf">pdf</a>] </li>
</ul>
</td>
</tr>
<tr>
<td>Lecture 13</td>
<td>11/5</td>
<td>K-means. Mixture of Gaussians. Expectation Maximization.</td>
<!-- <td> </td> -->
</tr>
<tr>
<td>Lecture 14</td>
<td> 11/7 </td>
<td>Factor Analysis.</td>
<!-- <td></td> -->
</tr>
<tr>
<td>Lecture 15</td>
<td> 11/12 </td>
<td>Principal Component Analysis. Independent Component Analysis.</td>
<!-- <td></td> -->
</tr>
<tr>
<td>Lecture 16</td>
<td> 11/14</td>
<td>MDPs. Bellman Equations.</td>
<!-- <td></td> -->
</tr>
<tr>
<td>Section</td>
<td>11/2</td>
<td colspan="2">
<strong>Discussion Section</strong>: Midterm Review [<a href="materials/cs229-mt-review.pdf">pdf</a>]
</td>
</tr>
<tr style="text-align:center; vertical-align:middle;background-color:#FFF2F2">
<td>A3</td>
<td>10/31</td>
<td colspan="3" style="text-align:center; vertical-align:middle;">
<strong>Problem Set 3</strong> <a href="problem-sets/ps3/">[directory]</a>
</td>
</tr>
<tr style="text-align:center; vertical-align:middle;background-color:#FFF2F2">
<td>Midterm</td>
<td>11/7</td>
<td colspan="2">
<span style="text-align: left;">We will have a take-home midterm. All details are posted <a href="https://piazza.com/class/jkbylqx4kcp1h3?cid=24">on Piazza</a>.</span>
</td>
</tr>
<tr>
<td>Section</td>
<td>11/16</td>
<td colspan="2">
<strong>Discussion Section</strong>: canceled
</td>
</tr>
<tr style="text-align:center; vertical-align:middle;background-color:#FFF2F2">
<td>Project</td>
<td> 11/16 </td>
<td colspan="2">Project milestones due 11/16 at <strong>11:59pm</strong>.</td>
</tr>
<!--<tr>
<td colspan="4" style="text-align:center; vertical-align:middle;background-color:#fffde7">
<strong>Reinforcement learning and control</strong> (4 classes)
</td>
</tr>-->
<tr>
<td>Lecture 17 </td>
<td> 11/26 </td>
<td> Value Iteration and Policy Iteration. LQR. LQG. </td>
<td rowspan="4">
<strong>Class Notes</strong>
<ul>
<li>Reinforcement Learning and Control [<a href="notes/cs229-notes12.pdf">pdf</a>]</li>
<li>LQR, DDP and LQG [<a href="notes/cs229-notes13.pdf">pdf</a>]</li>
</ul>
</td>
</tr>
<tr>
<td>Lecture 18</td>
<td> 11/28 </td>
<td>Q-Learning. Value function approximation.</td>
<!-- <td> </td> -->
</tr>
<tr>
<td>Lecture 19</td>
<td> 12/3 </td>
<td>Policy Search. REINFORCE. POMDPs.</td>
<!-- <td></td> -->
</tr>
<tr>
<td>Lecture 20</td>
<td> 12/5 </td>
<td>Optional topic. Wrap-up.</td>
<!-- <td></td> -->
</tr>
<tr style="text-align:center; vertical-align:middle;background-color:#FFF2F2">
<td>A4</td>
<td>11/14</td>
<td colspan="3" style="text-align:center; vertical-align:middle;">
<strong>Problem Set 4</strong> <a href="problem-sets/ps4/">[directory]</a>
</td>
</tr>
<tr>
<td>Section</td>
<td>11/30</td>
<td colspan="2">
<strong>Discussion Section</strong>: On critiques of Machine Learning [<a href="materials/critiques-ml.pdf">slides</a>]
</td>
</tr>
<tr>
<td>Section</td>
<td>12/07</td>
<td colspan="2">
<strong>Discussion Section</strong>: Convolutional Neural Networks
</td>
</tr>
<tr style="text-align:center; vertical-align:middle;background-color:#FFF2F2">
<td>Project</td>
<td>12/10</td>
<td colspan="2" style="text-align:center; vertical-align:middle;">
<strong>Project poster PDF</strong> and project recording (some teams) due at 11:59 pm.<br>
</td>
</tr>
<tr style="text-align:center; vertical-align:middle;background-color:#FFF2F2">
<td>Project</td>
<td> 12/11 </td>
<td colspan="2"> Poster presentations from 8:30-11:30am. Venue and details to be announced.</td>
</tr>
<tr style="text-align:center; vertical-align:middle;background-color:#FFF2F2">
<td>Project</td>
<td> 12/13 </td>
<td colspan="2">Final writeup due at <strong>11:59pm</strong> (no late days).</td>
</tr>
<tr class="warning" id="opt">
<td colspan="4">
<b>Supplementary Notes</b>
<ol>
<li>Binary classification with +/-1 labels [<a href="extra-notes/loss-functions.pdf">pdf</a>]</li>
<li>Boosting algorithms and weak learning [<a href="extra-notes/boosting.pdf">pdf</a>] </li>
<li>Functional after implementing stump_booster.m in PS2. [<a href="extra-notes/boosting_example.m">here</a>] </li>
<li>The representer theorem [<a href="extra-notes/representer-function.pdf">pdf</a>]</li>
<li>Hoeffding's inequality [<a href="extra-notes/hoeffding.pdf">pdf</a>] </li>
</ol></td>
</tr>
<tr class="alert">
<td colspan="4">
<b>Section Notes</b>
<ol>
<li id="la">Linear Algebra Review and Reference [<a href="section/cs229-linalg.pdf">pdf</a>]</li>
<li>Probability Theory Review [<a href="section/cs229-prob.pdf">pdf</a>] </li>
<!--<li>Files for the Matlab tutorial: [<a href="http://cs229.stanford.edu/materials/MATLAB_Session.pdf">pdf</a>] [<a href="section/matlab/sigmoid.m">sigmoid.m</a>] [<a href="section/matlab/logistic_grad_ascent.m">logistic_grad_ascent.m</a>] [<a href="http://cs229.stanford.edu/materials/matlab_session.m">matlab_session.m</a>] </li>-->
<li>Convex Optimization Overview, Part I [<a href="section/cs229-cvxopt.pdf">pdf</a>]</li>
<li>Convex Optimization Overview, Part II [<a href="section/cs229-cvxopt2.pdf">pdf</a>] </li>
<li>Hidden Markov Models [<a href="section/cs229-hmm.pdf">pdf</a>] </li>
<li>The Multivariate Gaussian Distribution [<a href="section/gaussians.pdf">pdf</a>] </li>
<li>More on Gaussian Distribution [<a href="section/more_on_gaussians.pdf">pdf</a>] </li>
<li>Gaussian Processes [<a href="section/cs229-gaussian_processes.pdf">pdf</a>] </li>
</ol></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>Matlab resources: Here are a couple of Matlab tutorials that you might find helpful: <a href="http://www.math.ucsd.edu/~bdriver/21d-s99/matlab-primer.html">http://www.math.ucsd.edu/~bdriver/21d-s99/matlab-primer.html</a> and <a href="http://www.math.mtu.edu/~msgocken/intro/node1.html">http://www.math.mtu.edu/~msgocken/intro/node1.html</a>. For emacs users only: If you plan to run Matlab in emacs, here are <a href="http://cs229.stanford.edu/materials/matlab.el">matlab.el</a>, and a helpful <a href="http://cs229.stanford.edu/materials/emacs">.emac's</a> file.<br></li>
<li>Octave resources: For a free alternative to Matlab, check out <a href="http://www.gnu.org/software/octave/">GNU Octave</a>. The official documentation is available <a href="http://www.gnu.org/software/octave/doc/interpreter/">here</a>. Some useful tutorials on Octave include <a href="http://en.wikibooks.org/wiki/Octave_Programming_Tutorial">http://en.wikibooks.org/wiki/Octave_Programming_Tutorial</a> and <a href="http://www-mdp.eng.cam.ac.uk/web/CD/engapps/octave/octavetut.pdf">http://www-mdp.eng.cam.ac.uk/web/CD/engapps/octave/octavetut.pdf</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/">NIPS</a>(all old NIPS 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>
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