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2000-01-03-schedule.html
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2000-01-03-schedule.html
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<style type="text/css">
td { padding:5px; }
</style>
<h2 id="schedule">Schedule</h2>
<table class="table table-striped">
<thead class="thead-inverse"><tr><th>Date</th><th width="60%">Description</th><th>Deadlines</th></tr></thead>
<tbody>
<tr>
<td><b>Week 1</b><br />15 Aug
</td>
<td>
<strong>
Motivation / Likelihood-based Models Part I: Autoregressive Models
</strong>
<br />
[ « <a href="#" data-toggle="#div1">Recording @ YouTube </a> ]
<div id="div1" style="display:none">
<iframe width="700" height="500" src="https://www.youtube.com/embed/i4Y5e9f2gcE" frameborder="0" allow="accelero\
meter; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe>
</div>
</td>
<td>
</td>
</tr>
<tr>
<td><b>Week 2</b><br />22 Aug
</td>
<td>
<strong>
Likelihood-based Models: Autoregressive Models / Flow Models
</strong>
<br />
[ « <a href="#" data-toggle="#div2">Recording @ YouTube </a> ]
<div id="div2" style="display:none">
<iframe width="700" height="500" src="https://www.youtube.com/embed/_jm5tdV3CXs" frameborder="0" allow="accelero\
meter; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe>
</div>
</td>
<td>
</td>
</tr>
<tr>
<td><b>Week 3</b><br />29 Aug
</td>
<td><strong>
Lossless Compression / Flow Models
</strong>
<br />
[ « <a href="#" data-toggle="#div3">Recording @ YouTube </a> ]
<div id="div3" style="display:none">
<iframe width="700" height="500" src="https://www.youtube.com/embed/l9aX_tHJGek" frameborder="0" allow="accelero\
meter; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe>
</div>
</td>
<td>
</td>
</tr>
<tr>
<td><b>Week 4</b><br />5 Sep
</td>
<td>
<strong>
Lecture 3a: Likelihood-based Models Part II: Flow Models (ctd) (same slides as week 2) /
Lecture 3b: Latent Variable Models - part 1
</strong>
</td>
<td>
</td>
</tr>
<tr>
<td><b>Week 5</b><br />12 Sep
</td>
<td>
<strong>
Lecture 4a: Latent Variable Models - part 2 /
Lecture 4b: Bits-Back Coding
</strong>
</td>
<td>
</td>
</tr>
<tr>
<td><b>Week 6</b><br />19 Sep
</td>
<td>
<strong>
Lecture 5a: Latent Variable Models - wrap-up (same slides as Latent Variable Models - part 2) /
Lecture 5b: ANS coding (same slides as bits-back coding) /
Lecture 5c: Implicit Models / Generative Adversarial Networks
</strong>
<td>Preliminary project titles and team members due on Slack's <code>#projects</code></td>
</tr>
<tr>
<td><b>Recess Week</b><br />26 Sep
</td>
<td>
<strong>
Lecture 6a: Implicit Models / Generative Adversarial Networks (ctd) (same slides as 5c) /
Lecture 6b: Non-Generative Representation Learning [UPDATED 3/24]
</strong>
</td>
<td>
</td>
</tr>
<tr>
<td><b>Week 7</b><br />3 Oct
</td>
<td>
<strong>
Lecture 7: Non-Generative Representation Learning (same slides as 6b)
</strong>
</td>
<td>Preliminary abstracts due to <code>#projects</code>
</td>
</tr>
<tr>
<td><b>Week 8</b><br />10 Oct
</td>
<td>
<strong>
Lecture 8a: Strengths/Weaknesses of Unsupervised Learning Methods Covered Thus Far /
Lecture 8b: Semi-Supervised Learning /
Lecture 8c: Guest Lecture by Ilya Sutskever
</strong>
</td>
<td>
</td>
</tr>
<tr>
<td><b>Week 9</b><br />17 Oct
</td>
<td>
<strong>
Lecture 9a: Unsupervised Distribution Alignment /
Lecture 9b: Guest Lecture by Alyosha Efros
</strong>
</td>
<td>
</td>
</tr>
<tr>
<td><b>Week 10</b><br />24 Oct
</td>
<td>
<strong>
Lecture 10: Language Models (Alec Radford)
</strong>
</td>
<td>
</td>
</tr>
<tr>
<td><b>Week 11</b><br />31 Oct
</td>
<td>
<strong>
<em>No lecture due to the <a href="https://wing.comp.nus.edu.sg/~ssnlp/">Singapore Symposium on Natural Language Processing</a> (SSNLP '19).</em>
</strong>
</td>
<td>
</td>
</tr>
<tr>
<td><b>Week 12</b><br />7 Nov
</td>
<td>
<strong>
Lecture 11: Representation Learning in Reinforcement Learning
</strong>
</td>
<td>
</td>
</tr>
<tr>
<td><b>Week 13</b><br />14 Nov
</td>
<td>
<strong>
TBA
</strong>
</td>
<td>Participation on evening of 15th STePS
</td>
</tr>
</td></tr></tbody></table>