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index.md

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Welcome everyone!
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In the New Member Education Program (NMEP), we'll be paving a sturdy ML foundation for you—from classical ML to diffusion models. Come hungry to learn and get to know the rest of your class!
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Your instructors this semester are TBD!
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Your instructor this semester is Surya Krishnapillai!
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### Schedule
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<tr> <th style="max-width: 30px;">Week</th> <th>Date</th> <th>Lecture</th> <th>Assignments</th> <th>Lecturer(s)</th> </tr>
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<tr>
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<td style="max-width: 30px;">1</td>
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<td>Sep 15</td>
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<td>Intro + Background Review
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<td>Feb 9</td>
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<td>Introduction + Math Review
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(<a href="https://docs.google.com/presentation/d/1bjh2TxylNo9ICEUp-4cGwDpdBBQUuvf6BmNdUQEF6AY">slides</a>)
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</td>
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</td>
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<td>
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Aaditya, Tejas
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Surya
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</td>
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</tr>
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<tr>
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<td style="max-width: 30px;">2</td>
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<td>Sep 22</td>
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<td>Feb 16</td>
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<td>Classical ML
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(<a href="https://docs.google.com/presentation/d/1K2fFV9_dYichrDxjXkIZ04aJ_cgfNfeIc6JI5hesdQY">slides</a>)
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</td>
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<td>
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<span class="label label-yellow"><strong>Homework 1</strong> (due 9/26)</span>
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<a href="/nmep/assets/hw1/hw1-math.pdf">Math</a>
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</td>
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<td>Aaditya, Aditya, Tanmayi</td>
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<td>Surya</td>
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</tr>
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<tr>
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<td style="max-width: 30px;">3</td>
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<td>Sep 29</td>
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<td>Feb 23</td>
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<td>Deep Learning (<a href="https://docs.google.com/presentation/d/14RE7f7HXmqpzRjGxOvJunsJ-vnWSvi7ENvag4pGrW1c/">slides</a>)</td>
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<td>
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<span class="label label-red"><strong>Reading</strong></span>
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<span class="label label-yellow"><strong>Homework 2</strong> (due 10/6)</span>
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<a href="https://drive.google.com/drive/folders/1fSEXCMcF8t8U4UCQDlkncTFR5b72KhPs">Word Embeddings</a>
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</td>
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<td>Aaditya, Aditya, Tanmayi</td>
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<td>Surya</td>
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</tr>
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<tr>
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<td style="max-width: 30px;">4</td>
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<td>Oct 6</td>
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<td>March 2</td>
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<td>Computer Vision (<a href="https://docs.google.com/presentation/d/15xuS2jh0KqBKpTMyNwb-DhgsvEER2-A8IFNwBd6RzZE/edit?usp=sharing">slides</a>)</td>
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<td>
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<span class="label"><strong>Lecture Exercise</strong></span>
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<span class="label label-yellow"><strong>Homework 3</strong> (due 10/20)</span>
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<a href="docs/homeworks/hw3.html">Model Zhu</a>
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</td>
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<td>Tim & Andrew</td>
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<td>Tim, Andrew</td>
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</tr>
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<td style="max-width: 30px;">5</td>
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<td>Oct 13</td>
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<td>March 9</td>
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<td>NLP + Transformers I (<a href="https://docs.google.com/presentation/d/1QAqkpaoEulM478d3REZHvZYb_uNEQe8XptfVyPF_I4c/edit?usp=sharing">slides</a>)</td>
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<td></td>
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<td>Bharat, Tejas</td>
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<td>Surya</td>
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</tr>
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<tr>
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<td style="max-width: 30px;">6</td>
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<td>Oct 20</td>
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<td>March 16</td>
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<td>Transformers II + LLMs (<a href="https://docs.google.com/presentation/d/11Cg3YT4JDLdou8v18p7sruhxOvNXnLt8swQL5sEkf2E/edit?usp=sharing">slides</a>) </td>
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<td>
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<span class="label"><strong>Worksheet</strong></span>
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<span class="label label-yellow"><strong>Homework 4</strong> (due 3/28)</span>
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<a href="docs/homeworks/hw4.html">Transformers!</a>
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</td>
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<td>Aaditya</td>
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<td>Tejas</td>
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</tr>
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<tr>
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<td style="max-width: 30px;">7</td>
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</tr>
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<tr>
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<td style="max-width: 30px;">10</td>
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<td>Nov 3</td>
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<td>State Space Models and Self-Supervised Learning(<a href="https://docs.google.com/presentation/d/1_whj4gntfZ35t_3uzTJLfMXKF7DJouAzeMIdG_iQPGM/edit?usp=sharing">slides</a>)</td>
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<td>March 30</td>
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<td>TBD(<a href="https://docs.google.com/presentation/d/1_whj4gntfZ35t_3uzTJLfMXKF7DJouAzeMIdG_iQPGM/edit?usp=sharing">slides</a>)</td>
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<td>
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<span class="label"><strong>Lecture Exercise</strong></span>
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<!-- <a href="https://github.com/tejasprabhune/lec10-diffusion">U-Net</a> -->
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</td>
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<td>Tejas, Julia</td>
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<td>TBD</td>
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</tr>
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<tr>
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<td style="max-width: 30px;">8</td>
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<td>Nov 10</td>
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<td>GANs and Diffusion(<a href="https://docs.google.com/presentation/d/1EK1BM6dbD8GslEg218Qfz579cg_KcQC6T91pF4lhjx4/edit?usp=sharing">slides</a>)</td>
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<td>Apr 6</td>
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<td>Generative Models(<a href="https://docs.google.com/presentation/d/1EK1BM6dbD8GslEg218Qfz579cg_KcQC6T91pF4lhjx4/edit?usp=sharing">slides</a>)</td>
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<td>
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<span class="label label-yellow"><strong>Homework 5 Part 1</strong> (due Nov 17)</span>
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<a href="https://colab.research.google.com/drive/1FPC6aq6oye9VMVNYb837pCwj3Bv9zsBy?usp=sharing">U-Nets and Diffusion!</a>
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</td>
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<td>Aditya, Aaditya</td>
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<td>Surya</td>
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</tr>
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<tr>
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<td style="max-width: 30px;">9</td>
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<td>Nov 17</td>
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<td>RL(<a href="https://docs.google.com/presentation/d/1NU4cYmHmOZGF3L_j78qVQ1LKqcu7dk7dtSzio1kn0iA/edit?usp=sharing">slides</a>)</td>
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<td>Apr 13</td>
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<td>Reinforcement Learning(<a href="https://docs.google.com/presentation/d/1NU4cYmHmOZGF3L_j78qVQ1LKqcu7dk7dtSzio1kn0iA/edit?usp=sharing">slides</a>)</td>
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<td>
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<!--<span class="label">
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<strong>Lecture Exercise</strong>
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</span>
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<a href="https://colab.research.google.com/drive/1A8OUfUPW4DOSbQ8Ieb1soa_AsPR7IQOy?usp=sharing">Ant PPO</a> -->
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</td>
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<td>Tanmayi</td>
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<td>Surya</td>
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</tr>
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<td style="max-width: 30px;">11</td>
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<td>Nov 24</td>
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<td>Apr 20</td>
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<td>AI Safety</td>
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<td><span class="label label-green"><strong>Final Project Checkpoint</strong></span></td>
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<td>Andrew</td>
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<td>Prakrat</td>
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</tr>
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<tr>
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<td style="max-width: 30px;">12</td>

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