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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>CS4641 Summer 2013</title>
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="description" content="">
<meta name="author" content="">
<!-- Le styles -->
<link href="css/bootstrap.css" rel="stylesheet">
<style type="text/css">
body {
padding-top: 60px;
padding-bottom: 40px;
}
.sidebar-nav {
padding: 9px 0;
}
@media (max-width: 980px) {
/* Enable use of floated navbar text */
.navbar-text.pull-right {
float: none;
padding-left: 5px;
padding-right: 5px;
}
}
</style>
<link href="css/bootstrap-responsive.css" rel="stylesheet">
<!-- HTML5 shim, for IE6-8 support of HTML5 elements -->
<!--[if lt IE 9]>
<script src="js/html5shiv.js"></script>
<![endif]-->
<!-- Fav and touch icons -->
<link rel="apple-touch-icon-precomposed" sizes="144x144" href="ico/apple-touch-icon-144-precomposed.png">
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<link rel="apple-touch-icon-precomposed" href="ico/apple-touch-icon-57-precomposed.png">
<link rel="shortcut icon" href="ico/favicon.png">
</head>
<body>
<div class="navbar navbar-inverse navbar-fixed-top">
<div class="navbar-inner">
<div class="container-fluid">
<button type="button" class="btn btn-navbar" data-toggle="collapse" data-target=".nav-collapse">
<span class="icon-bar"></span>
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="brand" href="#">CS 4641</a>
<div class="nav-collapse collapse">
<ul class="nav">
<li class="active"><a href="index.html">Home</a></li>
<li><a href="schedule.html">Schedule</a></li>
</ul>
</div><!--/.nav-collapse -->
</div>
</div>
</div>
<div class="container-fluid">
<div class="row-fluid">
<div class="span3">
<div class="well sidebar-nav">
<ul class="nav nav-list">
<li class="nav-header">Text</li>
<li><a href="http://www.cs.cmu.edu/~tom/mlbook.html">Required</a></li>
<li><a href="http://www.stat.cmu.edu/~larry/all-of-statistics/">Stats</a></li>
<li class="nav-header">Other</li>
<li><a href="http://www.kaggle.com/">Kaggle</a></li>
</ul>
</div><!--/.well -->
</div><!--/span-->
<div class="span9">
<h2>Machine Learning</h2>
<b>Summer 2013</b>
<p>Machine Learning is that area of Artificial Intelligence that is concerned with computational artifacts that modify and improve their performance through experience. The area is concerned with issues both theoretical and practical. We will cover a variety of topics, including: statistical supervised and unsupervised learning methods, randomized search algorithms, Bayesian learning methods, and reinforcement learning. The course also covers theoretical concepts such as inductive bias, the PAC and Mistake-bound learning frameworks, minimum description length principle, and Ockham's Razor. In order to ground these methods the course includes some programming and involvement in a number of projects.</p>
<table class="table">
<caption>Schedule</caption>
<thead>
<tr><th>Date</th><th>Topic</th></tr>
</thead>
<tbody>
<tr><td>May 14</td><td>Introduction and Overview</td></tr>
<tr><td>May 16</td><td>Decision Trees and Neural Networks</td></tr>
<tr><td>May 21</td><td>Overfitting and Information Theory</td></tr>
<tr><td>May 23</td><td>Computational Learning Theory</td></tr>
<tr><td>May 28</td><td>Instance Based Learning, Support Vector Machines</td></tr>
<tr><td>May 30</td><td>Boosting, Ensemble Learning</td></tr>
<tr><td>Jun 4</td><td>Bayesian Learning</td></tr>
<tr><td>Jun 6</td><td>Sampling and Bayesian Inference</td></tr>
<tr><td>Jun 11</td><td>Midterm</td></tr>
<tr><td>Jun 13</td><td>Midterm Review</td></tr>
<tr><td>Jun 14</td><td>Drop Day</td></tr>
<tr><td>Jun 18</td><td>Randomized Optimization</td></tr>
<tr><td>Jun 20</td><td>Clustering</td></tr>
<tr><td>Jun 25</td><td>EM and the Impossibility results</td></tr>
<tr><td>Jun 27</td><td>Feature selection</td></tr>
<tr><td>Jul 2</td><td>Feature Transformation</td></tr>
<tr><td>Jul 4</td><td>Official School Holiday</td></tr>
<tr><td>Jul 9</td><td>Markov Decision Processes</td></tr>
<tr><td>Jul 11</td><td>Reinforcement Learning</td></tr>
<tr><td>Jul 16</td><td>Reinforcement Learning</td></tr>
<tr><td>Jul 18</td><td>Game Theory</td></tr>
<tr><td>Jul 23</td><td>Project Presentations</td></tr>
<tr><td>Jul 26</td><td>Project Presentations</td></tr>
<tr><td>Jul 30</td><td>Finals</td></tr>
</tbody>
</table>
<div class="row-fluid">
<div class="span4">
<h2>Schedule</h2>
<p>We will cover Supervised Learning, Complexity Theory and Optimization in the first half of the semester. The rest of the semester will be dedicated to Unsupervised Learning and Reinforcement Learning.</p>
<p><a class="btn" href="schedule.html">View details »</a></p>
</div><!--/span-->
<div class="span4">
<h2>Heading</h2>
<p>Donec id elit non mi porta gravida at eget metus. Fusce dapibus, tellus ac cursus commodo, tortor mauris condimentum nibh, ut fermentum massa justo sit amet risus. Etiam porta sem malesuada magna mollis euismod. Donec sed odio dui. </p>
<p><a class="btn" href="#">View details »</a></p>
</div><!--/span-->
<div class="span4">
<h2>Heading</h2>
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<p><a class="btn" href="#">View details »</a></p>
</div><!--/span-->
</div><!--/row-->
<hr>
<footer>
<p>© Pushkar Kolhe</p>
</footer>
</div><!--/.fluid-container-->
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<script src="js/bootstrap-transition.js"></script>
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