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<html><head>
<title>CS 229: Machine Learning (Course handouts)</title>
<style type="text/css"></style></head><body bgcolor="white" lang="EN-US" link="blue" vlink="blue" style="tab-interval:.5in">
<div class="Section1">
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<p class="MsoNormal">
<a href="http://www.stanford.edu/class/cs229/">
<img width="64" height="64" id="_x0000_i1025" src="stanford.seal64.gif" alt="Stnf" align="middle">
</a>
</p>
</td>
<td style="padding: 0.75pt;" align="left">
<p class="MsoNormal"><span style="font-size: 18pt;">CS 229</span><br>
<span style="font-size: 18pt;">Machine Learning<br>
Course Materials
</span></p></td>
</tr>
</tbody>
</table>
<div class="MsoNormal" align="center" style="text-align:center">
<hr size="2" width="100%" align="center">
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50%;background-attachment:
scroll;background-repeat:repeat">
<tbody><tr>
<td style="padding:2.25pt 2.25pt 2.25pt 2.25pt">
<p class="MsoNormal"><b><span style="font-size:13.5pt;color:white">Handouts and Problem Sets</span></b></p>
</td>
</tr>
</tbody></table>
<p class="MsoNormal" style="margin-left:.25in"><!--[if !supportEmptyParas]--> <!--[endif]--><o:p></o:p></p>
<ul style="margin-top:0in" type="disc">
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="info.html">Handout #1: Course Information (HTML)</a>
<a href="materials/Handout1.pdf">
(pdf)
</a>
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="schedule.html">Handout #2: Course Schedule
(HTML)</a>
<a href="materials/Handout2.pdf">
(pdf)
</a>
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="materials/coverSheet.pdf">Handout #3: Cover Sheet</a>
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="materials/practice-midterm-2010.pdf">Handout #4: Practice Midterm 1 </a> Solution:
<a href="materials/midterm-2010-solutions.pdf">Solution</a>
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="materials/midterm_aut2014.pdf">Handout #5: Practice Midterm 2 </a> Solution:
<a href="midtermsolution/midterm_aut2014(final)-sol.pdf">Solution</a>
</li>
<li>
<a href="materials/ps1.pdf">Problem Set 1 (pdf)</a>
Data:
<a href="ps/ps1/q1x.dat">q1x.dat</a>,
<a href="ps/ps1/q1y.dat">q1y.dat</a>,
<a href="ps/ps1/q2x.dat">q2x.dat</a>,
<a href="ps/ps1/q2y.dat">q2y.dat</a>
Solution:
<a href="ps/ps1/ps1sol.pdf">Solution (pdf)</a>
</li>
<li>
<a href="materials/ps2.pdf">Problem Set 2 (pdf)</a>
Data:
<a href="ps/ps2/ps2.zip">ps2.zip</a>
Solution:
<a href="ps/ps2/ps2sol.pdf">Solution (pdf)</a>
</li>
<li>
<a href="materials/ps3.pdf">Problem Set 3 (pdf)</a>
Solution:
<a href="ps/ps3/ps3sol.pdf">Solution (pdf)</a>
</li>
<li>
<a href="ps/ps4/ps4.pdf">Problem Set 4 (pdf)</a>
Solution:
<a href="ps/ps4/ps4sol.pdf">Solution (pdf)</a>
</li>
</ul>
<!--
<a href="review.ps">Midterm review form (ps)</a><a href="review.pdf">(pdf)</a>
!-->
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50%;background-attachment:
scroll;background-repeat:repeat">
<tbody><tr>
<td style="padding:2.25pt 2.25pt 2.25pt 2.25pt">
<p class="MsoNormal"><b><span style="font-size:13.5pt;color:white">Lecture
Notes</span></b></p>
</td>
</tr>
</tbody></table>
<p>
<!--These are a preliminary version of the course's lecture notes.
The final version of each set of notes will be posted
later. <p>
!-->
</p><ul style="margin-top:0in" type="disc">
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="notes/cs229-notes1.ps">Lecture notes 1 (ps)</a>
<a href="notes/cs229-notes1.pdf"> (pdf) </a>
Supervised Learning, Discriminative Algorithms
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="notes/cs229-notes2.ps">Lecture notes 2 (ps)</a>
<a href="notes/cs229-notes2.pdf"> (pdf) </a>
Generative Algorithms
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="notes/cs229-notes3.ps">Lecture notes 3 (ps)</a>
<a href="notes/cs229-notes3.pdf"> (pdf) </a>
Support Vector Machines
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="notes/cs229-notes4.ps">Lecture notes 4 (ps)</a>
<a href="notes/cs229-notes4.pdf"> (pdf) </a>
Learning Theory
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="notes/cs229-notes5.ps">Lecture notes 5 (ps)</a>
<a href="notes/cs229-notes5.pdf"> (pdf) </a>
Regularization and Model Selection
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="notes/cs229-notes6.ps">Lecture notes 6 (ps)</a>
<a href="notes/cs229-notes6.pdf"> (pdf) </a>
Online Learning and the Perceptron Algorithm. (optional reading)
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="notes/cs229-notes7a.ps">Lecture notes 7a (ps)</a>
<a href="notes/cs229-notes7a.pdf"> (pdf) </a>
Unsupervised Learning, k-means clustering.
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="notes/cs229-notes7b.ps">Lecture notes 7b (ps)</a>
<a href="notes/cs229-notes7b.pdf"> (pdf) </a>
Mixture of Gaussians
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="notes/cs229-notes8.ps">Lecture notes 8 (ps)</a>
<a href="notes/cs229-notes8.pdf"> (pdf) </a>
The EM Algorithm
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="notes/cs229-notes9.ps">Lecture notes 9 (ps)</a>
<a href="notes/cs229-notes9.pdf"> (pdf) </a>
Factor Analysis
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="notes/cs229-notes10.ps">Lecture notes 10 (ps)</a>
<a href="notes/cs229-notes10.pdf"> (pdf) </a>
Principal Components Analysis
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="notes/cs229-notes11.ps">Lecture notes 11 (ps)</a>
<a href="notes/cs229-notes11.pdf"> (pdf) </a>
Independent Components Analysis
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="notes/cs229-notes12.ps">Lecture notes 12 (ps)</a>
<a href="notes/cs229-notes12.pdf"> (pdf) </a>
Reinforcement Learning and Control
</li>
</ul>
<table border="0" cellpadding="0" width="100%" bgcolor="#990000" style="width:100.0%;
mso-cellspacing:1.5pt;background:#990000;mso-padding-alt:0in 0in 0in 0in;
background-position-x:0%;background-position-y:
50%;background-attachment:
scroll;background-repeat:repeat">
<tbody><tr>
<td style="padding:2.25pt 2.25pt 2.25pt 2.25pt">
<p class="MsoNormal"><u7:p></u7:p><b><span style="font-size:13.5pt;color:white">Section Notes</span></b></p>
</td>
</tr>
</tbody></table>
<p class="MsoNormal" style="margin-left:.25in"><!--[if !supportEmptyParas]--> <!--[endif]--><o:p></o:p></p>
<ul style="margin-top:0in" type="disc">
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="section/cs229-linalg.pdf">Section notes 1 (pdf)</a>
Linear Algebra Review and Reference
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="section/cs229-prob.pdf">Section notes 2 (pdf)</a>
Probability Theory Review
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in">
Files for the Matlab tutorial: <a href="section/matlab/sigmoid.m">
sigmoid.m</a>, <a href="section/matlab/logistic_grad_ascent.m">
logistic_grad_ascent.m</a>,
<a href="section/matlab/matlab_session.m"> matlab_session.m </a>
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="section/cs229-cvxopt.ps">Section notes 4 (ps)</a>
<a href="section/cs229-cvxopt.pdf"> (pdf) </a>
Convex Optimization Overview, Part I
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="section/cs229-cvxopt2.ps">Section notes 5 (ps)</a>
<a href="section/cs229-cvxopt2.pdf"> (pdf) </a>
Convex Optimization Overview, Part II
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="section/cs229-hmm.ps">Section notes 6 (ps)</a>
<a href="section/cs229-hmm.pdf"> (pdf) </a>
Hidden Markov Models
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="section/gaussians.pdf">Section notes 7 (pdf)</a>
The Multivariate Gaussian Distribution
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="section/more_on_gaussians.pdf">Section notes 8 (pdf)</a>
More on Gaussian Distribution
</li>
<li class="MsoNormal" style="mso-list:l1 level1 lfo3;tab-stops:list .5in"><a href="section/cs229-gaussian_processes.pdf">Section notes 9 (pdf)</a>
Gaussian Processes
</li>
</ul>
<p class="MsoNormal"> <u7:p></u7:p><o:p></o:p></p>
<table border="0" cellpadding="0" width="100%" bgcolor="#990000" style="width:100.0%;
mso-cellspacing:1.5pt;background:#990000;mso-padding-alt:0in 0in 0in 0in;
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50%;background-attachment:
scroll;background-repeat:repeat">
<tbody><tr>
<td style="padding:2.25pt 2.25pt 2.25pt 2.25pt">
<p class="MsoNormal"><u7:p></u7:p><b><span style="font-size:13.5pt;color:white">Other resources </span></b></p>
</td>
</tr>
</tbody></table>
<p></p>
<p>
<b> Advice on applying machine learning: </b>
Slides from Andrew's lecture on getting machine learning
algorithms to work in practice can be found
<a href="materials/ML-advice.pdf">here</a>.
</p>
<p>
<b> Previous projects:
</b> A list of
last year's final projects can be found
<a href="projects2011.html">
here</a>.
</p>
<p>
<b> Matlab resources: </b>
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="materials/matlab.el">matlab.el</a>, and
a helpful <a href="materials/emacs">.emac's file</a>.
</p>
<p>
<b> Octave resources: </b>
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>
<!--<a href="http://homepages.nyu.edu/~kpl2/dsts6/octaveTutorial.html">http://homepages.nyu.edu/~kpl2/dsts6/octaveTutorial.html</a> and <a href="http://wiki.aims.ac.za/mediawiki/index.php/Octave">http://wiki.aims.ac.za/mediawiki/index.php/Octave</a>-->.
</p>
<p>
<b>Data: </b>
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.
</p><p>
<b>Viewing PostScript and PDF files:</b> Depending on the computer you are using, you may be able to download a <a href="http://www.cs.wisc.edu/~ghost/">PostScript viewer</a> or <a href="http://www.adobe.com/products/acrobat/readstep2_allversions.html">PDF viewer</a> for
it if you don't already have one. </p>
<br>
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<hr size="2" width="100%" align="center">
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<p class="MsoNormal">Comments to <a href="mailto:[email protected]">[email protected]</a>
</p>
</td>
<td style="padding:0in 0in 0in 0in">
<p class="MsoNormal" align="right" style="text-align:right"><a href="http://www.stanford.edu/class/cs229/">Home Page</a> </p>
</td>
</tr>
</tbody></table>
<br>
</div></body></html>