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prerequisites.html
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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
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<title>Advanced Topics In Computer Vision And Deep Learning</title>
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<body>
<div class="container">
<table border="0" align="center">
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<td width="160" align="right"><a href="https://www.weizmann.ac.il"><img src="pics/weizmann_logo.jpg" width="240" height="126" border="0" /></a></td>
<td width="623" align="center" valign="middle"><h3>Weizmann Institute of Science</h3>
<p><strong id="docs-internal-guid-be9d3617-7fff-49eb-ccd0-d78bcfc8d0b2">20214022</strong></p>
<span class="title">Advanced Topics In Computer Vision <br> And Deep Learning</span></td>
<td width="160" align="left"><a href="https://www.weizmann.ac.il/math/waic/home"><img src="pics/waic_logo.png" width="100" height="95" border="0" /></a></td>
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<tr><td colspan="3" align="center"><h3>Spring 2021</h3></td></tr>
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<td colspan="4" align="center">
<span class="menubar"> [
<a href="index.html">Home</a> |
<a href="schedule.html">Schedule</a> |
<a href="prerequisites.html">Prerequisites</a> |
<a href="useful.html">Useful Links</a>
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<br>
<br>
<br>
<h3> Prerequisites: </h3>
The course is intended to cover topics of research from the past year. Basic knowledge is assumed. <br>
Students who took the Intro to Vision course can cover on their own the topics below that were not taught. <br>
<ul>
<li>Deep Learning basics and applications. (<a href='http://cs231n.stanford.edu/'>Stanford's CS231n course</a> covers it all), In particular, familiarity with: <br>
<ul>
<li>Neural network optimization: Back propagation, SGD variants (Example: ADAM)
<li>Losses and Layers (Examples: Softmax, Batch-norm, Cross-entropy-loss)
<li>CNNs: Basics, applications, architectures (example: ResNet), Batch-Norm. <a href='https://www.youtube.com/watch?v=bNb2fEVKeEo&t=0s&index=6&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv'>[video1]</a>, <a href='https://www.youtube.com/watch?v=DAOcjicFr1Y&t=0s&index=10&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv'>[video2]</a>
<li>RNNs: Basics, LSTM, applications, combinations with CNNs (Example: basic image captioning). <a href='https://www.youtube.com/watch?v=6niqTuYFZLQ&t=3451s&index=11&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv'>[video]</a>
<li>GANs: Basics, applications, domain transfer (example: pix2pix) <a href='https://www.youtube.com/watch?v=5WoItGTWV54&t=0s&index=14&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv'>[video]</a> (The last part of the video)
<li>Basics of Deep Reinforcement learning: Shallow familiarity with basic methods is sufficient (Example: policy gradients)<br> <a href='https://www.youtube.com/watch?v=lvoHnicueoE&t=0s&index=15&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv'>[video]</a>
</ul>
<li>Classical Computer VIsion basics. (Basic course at the faculty covers it all)
</ul>
<h3> Course Requirements: </h3>
<ul>
<li>Attend all the lessons. In case of miluim, illness or other justified absence please let us know.</li>
<li>Read for each class the assigned reading material.</li>
<li>Prepare a presentation.</li>
</ul>
<h3> Grades will be given based on: </h3>
<ul>
<li>Quality and clarity of presentation. </li>
<li>Level of understanding of the material and the ability to present in a clear and simple way.</li>
<li>Active participation in the other lessons.</li>
<li>Fulfillment of reading assignment and attendance.</li>
</ul>
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