PatternRecognition/README.html


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<center><h2>IT 342: Pattern Recognition</h2></center>

<center><h2>Advice: Please, LOVE and UNDERSTAND probability, statistics, and linear algebra before you register for this course.</h2></center>

<h2>Resources</h2>
<ul>
    <li><a href="CourseSyllabus.pdf">Course syllabus</a>.</li>

    <li><a href="http://www.youtube.com/course?list=ECoK2Lr1miEm9scZv7zSAMENjMhcMMH2cU" target="_blank">Video Lectures.</a></li>

    <li><a href="http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html"target="_blank">Link to book webpage</a>. (You can download it in pdf format!). Other two great references (not mandatory for the course) are <a href="http://rii.ricoh.com/~stork/DHS.html" target="_blank">Duda, Hart, and Stork</a>; and <a href="http://research.microsoft.com/en-us/um/people/cmbishop/PRML/" target="_blank">Bishop.</a></li>

    <li><a href="LectureNotes/">Lecture Notes</a>

    <li><a href="Code">Code and Programs</a>.</li>

    <li><a href="ImportantTexts.pdf">List of useful texts.</a></li>

    <li><a href="http://www.ics.uci.edu/~mlearn/MLRepository.html" target="_blank">UCI Machine Learning Repository</a> has many real-life data sets.</li>

    <li><a href="https://www.datacamp.com/groups/data-science-2-pattern-recognition-and-statistical-learning" target="_blank">DataCamp assignments</a></li>

    <!--
    <li>Please, download <a href="SampleFinal2007.pdf">exam sample</a>. </li>
    -->
</ul>

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<!-- <h2><a href="../VideosOnDVD" target="_blank">Video Lectures on DVD.</a></h2> -->

<h2>Lectures:</h2>

<table>
    <tr><td width="30">01.</td><td width="600">Introduction</td></tr>
    <tr><td>02.</td>      <td>Introduction to Statistical Decision Theory I (Regression)</td></tr>

    <tr><td>03.</td>      <td>Introduction to Statistical Decision Theory II (Classification)</td></tr>

    <tr><td>04.</td>      <td>Introduction to Statistical Decision Theory III (Classification)</td></tr>

    <tr><td>05.</td>      <td>Getting to "Learning" I (Regression)</td></tr>

    <tr><td>06.</td>      <td>Getting to "Learning" II (Classification)</td></tr>

    <tr><td>07.</td>      <td>Linear Models for Regression: Least Mean Square</td></tr>

    <tr><td>08.</td>      <td>Linear Models for Regression: Centered Model</td></tr>

    <tr><td>09.</td>      <td>Linear Models for Regression: Performance</td></tr>

    <tr><td>10.</td>      <td>Linear Models for Regression: Data Preprocessing and Transformation</td></tr>

    <tr><td>11.</td>      <td>Bias-Variance Decomposition</td></tr>

    <tr><td>A1.</td>      <td>fast revision on basics of Probability</td></tr>

    <tr><td>12.</td>      <td>fast revision on basics of Statistics</td></tr>
</table>



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<h2>Assignments</h2>
<ol type="1">

    <!--        <li>Read the following: (1) Ch. 2 in text. (2) A summary of probabilistic foundations of the Statistical Pattern Recognition (SPR) in this <a href="SPRintroduction.pdf">article</a>, which is the first chapter in my dissertation. (3)The journal paper: Jain, Duin, and Jianchang (2000), Statistical Pattern Recognition: a review, PAMI, IEEE Transactions on, 22 (1).</li>-->

    <li>Ch2, Duda, Hart, and Stork: solve 2, 6, 7, 8, 9, 27, 33; and computer exercises 2, 3.</li>

    <li><a href="Assignments/HW4.pdf">LLR simulation</a></li>

    <li><a href="Assignments/HW5.pdf">Linear models</a></li>

    <li><a href="Assignments/HW6.pdf">Linear models (cont.)</a></li>

    <li><a href="Assignments/HW7.pdf">Linear models (cont.)</a>, and <a href="Assignments/HW8.pdf">this one</a></li>

    <li><a href="Assignments/HW9.pdf">Linear models for classification</a></li>

    <li><a href="Assignments/HW10.pdf">Linear models for classification (2)</a></li>

<pre>
<b>Problems on Appendix (revision):</b>
</pre>

    <li>Review the basics of Probability and Statistics then solve <a href="Assignments/Prob1.pdf">HW1</a>, <a href="Assignments/Prob3.pdf">HW2</a>, and <a href="Assignments/HW2.pdf">HW3</a></li>


</ol>

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<h2>Suggested Projects</h2>
Please, download <a href="ProjectsSpring2011.pdf">guidelines, and suggested projects</a>.


<!--<h2><FONT COLOR="#FB0000">Take-home midterm exam</FONT></h2>
<a href="MidTerm2011.pdf">This is the midterm exam</a>. Solve as much as you can.
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<h2><b>Announcements</b></h2>
Please, find here <a href="Exams">samples for exams.</a>

<!--For announcements, grades, and other postings, please visit the <a href="http://www.helwan.edu.eg:90/fcih/details.asp?id=155" target="_blank">TA's Webpage</a>.-->

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