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<?xml version="1.0" encoding="utf-8"?> <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" ""> <html xmlns="" xml:lang="en" lang="en"> <head> <meta name="generator" content= "HTML Tidy for Linux (vers 7 December 2008), see" /> <link rel="stylesheet" href=". ./css/vishystyle.css" type= "text/css" /> <title>Introduction to Machine Learning (CS 590 and STAT 598A)</title> <meta name="description" content= "Introduction to Computational Statistics" /> <meta name="keywords" content="Course Description" /> <meta name="resource-type" content="document" /> <meta name="distribution" content="global" /> <meta http-equiv="Content-Type" content= "text/html; charset=us-ascii" /> <script type="text/javascript"> //<![CDATA[ <!-- function switchMenu(obj) { var el = document.getElementById(obj); if ( != "none" ) { = 'none'; }else { = ''; } } //--> //]]> </script> </head> <body> <center> <h2> Introduction to Machine Learning (CS 590 and STAT 598A) <br /> CS 59000-030 and STAT 59800 VS1, Spring 2010 </h2> </center> <table summary="none" cellspacing="3"> <tr> <td width="25%"><b>Class</b></td> <td width="75%">9:00 - 10:15am TTh</td> </tr> <tr> <td width="25%"><b>Location</b></td> <td width="75%">LWSN B134</td> </tr> <tr> <td width="25%"><b>Instructor</b></td> <td width="75%">S V N Vishwanathan (email: vishy)</td> </tr> <tr> <td width="25%"><b>Office</b></td> <td width="75%">HAAS 232</td> </tr> <tr> <td width="25%"><b>Telephone</b></td> <td width="75%">765 494 0033</td> </tr> <tr> <td width="25%"><b>Office Hours</b></td> <td width="75%">Tue: 2 - 3pm or by appointment</td> </tr>
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</table> <div id="coursedescription"> <p> <a href="#" onclick="switchMenu('coursedescriptionpara');" title= "course description">Course Description</a></p> <div id="coursedescriptionpara" style="display:none"> <p> With the availability of cheap storage devices our ability to collect and store large amounts of data is increasing exponentially. Machine learning is a branch of applied statistics which aims to bring to bear tools from statistics in the analysis of such large datasets. This course is a biased journey through some of dominant concepts in machine learning. This is an INTRODUCTORY course in Machine Learning. As such, it will cover basic concepts from both computer science as well as statistics. In first part of the course we will review linear algebra, probability theory, and programming at a very brisk pace. In the next 3 - 4 weeks we will work on some basic machine learning algorithms such as k-means, k-nearest neighbors, Perceptron etc. Finally, we will switch gears and cover a number of more advanced topics. Students will have a chance to implement and test a machine learning algorithm of their choice as part of a medium-scale programming project.</p> </div> <div id="prerequisites"> <p><a href="#" onclick="switchMenu('prerequisitespara');" title=
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This note was uploaded on 02/23/2012 for the course STAT 598 taught by Professor Staff during the Spring '08 term at Purdue.

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introml -

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