| title | NMEP Spring 2026 |
|---|---|
| nav_order | 1 |
| layout | home |
Welcome everyone! In the New Member Education Program (NMEP), we'll be paving a sturdy ML foundation for you - from basic ML definitions to LLMs, generative models, and reinforcement learning. Come hungry to learn and get to know the rest of your class!
Your instructor this semester is Surya Krishnapillai!
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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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<td style="max-width: 30px;">1</td>
<td>Feb 9</td>
<td>Introduction + Math Review
(<a href="https://docs.google.com/presentation/d/1yi3AZ3YLPT01SFb6F8Z3WgTBsCUL7kzxPe4WOdaQmDk/edit?usp=sharing">slides</a>)
</td>
<td>
<span class="label"><strong>Lecture Exercise</strong></span>
<a href="https://drive.google.com/file/d/1p_QsJX3d3T3mSeePed0_CDieP39g83Pc/view?usp=drive_link">Rockfall</a>
<br>
<span class="label label-yellow"><strong>Homework 1</strong> (due Feb 23)</span>
<a href="/nmep/assets/hw1/hw1-math.pdf">Math</a>
<a href="https://colab.research.google.com/github/mlberkeley/nmep/blob/main/assets/hw1/hw1-numpy.ipynb">NumPy</a>
<a href="https://colab.research.google.com/github/mlberkeley/nmep/blob/main/assets/hw1/hw1-intro-pytorch.ipynb">PyTorch</a>
</td>
<td>Surya</td>
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<td style="max-width: 30px;">2</td>
<td>Feb 16</td>
<td>Classical Machine Learning
(<a href="https://docs.google.com/presentation/d/1Val7XJBUKo0OgYnJD4j7vmHLYOxqtDo9PkP5sKEy3IQ/edit?usp=sharing">slides</a>)
(<a href="https://docs.google.com/document/d/1K8C-xmWe5VAJBvodxH9-v8nYomVjt7olawJXjqMZhKY/edit?usp=sharing">notes</a>)
</td>
<td></td>
<td>Surya</td>
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<td style="max-width: 30px;">3</td>
<td>Feb 23</td>
<td>Deep Learning
(<a href="https://docs.google.com/presentation/d/1l6h2Lqfv86eMuB0kUU8Rwxq_mNv7M0A_yWoku_E5bTs/edit?usp=sharing">slides</a>)
(<a href="https://docs.google.com/document/d/1_vVDNg2L7n_R8kxTm_opW8AQXanQ9_TDr-K2dgAh7cM/edit?usp=sharing">notes</a>)
</td>
<td>
<span class="label label-red"><strong>Reading</strong></span>
<a href="https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf">How to read a paper</a>
<br>
<span class="label label-yellow"><strong>Homework 2</strong> (due March 2)</span>
<a href="docs/homeworks/hw2.html">Word Embeddings</a>
<a href="https://colab.research.google.com/drive/1FPTx1RXtBfc4MaTkf7viZZD4U2F9gtKN?usp=sharing">Micrograd</a>
</td>
<td>Surya</td>
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<td style="max-width: 30px;">4</td>
<td>March 2</td>
<td>Computer Vision
(<a href="https://docs.google.com/presentation/d/16FRcCaVF72MkKK63YkDuq-HRk-OC5hWlNooTzfbh5gM/edit?usp=sharing">slides</a>)
(<a href="https://docs.google.com/document/d/1z5Ik8tFjcGqz21di8_C1tGFZUJHq_GPODQHPV76JdkU/edit?usp=sharing">notes</a>)
</td>
<td>
<span class="label"><strong>Lecture Exercise</strong></span>
<a href="https://colab.research.google.com/drive/103Keq-lZgknscTKp7FEcUQHVZvrJM1dN?usp=sharing"> YOLO </a>
<br>
<span class="label label-yellow"><strong>Homework 3</strong> (due March 16)</span>
<a href="docs/homeworks/hw3.html">Model Zhu</a>
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<td>Tim, Andrew</td>
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<td style="max-width: 30px;">5</td>
<td>March 9</td>
<td>NLP + Transformers
(<a href="https://docs.google.com/presentation/d/1vdouen9dPhzHxi4D3dgYhrBxo5yORsTYHpThNm6aaoA/edit?usp=sharing">slides</a>)
(<a href="https://docs.google.com/document/d/1TrmyDARjxQw8tH2Yhd5cpDPem9ylhdhU8n4k3lV5WsQ/edit?usp=sharing">notes</a>)
</td>
<td>
<span class="label label-red"><strong>NMEP Midterm</strong> (March 15)</span>
</td>
<td>Surya, Saathvik</td>
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<td style="max-width: 30px;">6</td>
<td>March 16</td>
<td>Large Language Models
(<a href="https://docs.google.com/presentation/d/1oRCO5tL95G57owNAfvZ_7ZYH-KAiCitBYUNZsGz8qXA/edit?usp=sharing">slides</a>)
</td>
<td>
<span class="label label-yellow"><strong>Homework 4</strong> (due April 6)</span>
<a href="docs/homeworks/hw4.html">Transformers</a>
</td>
<td>Tejas</td>
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<td style="max-width: 30px;">7</td>
<td>March 30</td>
<td>SSL + Generative Models
(<a href="https://docs.google.com/presentation/d/1HU6GRjLliNj-AvaiSsRGD1qVINpJI9aOG30wPyxNloM/edit?usp=sharing">slides</a>)
(<a href="https://drive.google.com/file/d/1yk45gvyCsNLFYHZ1qUGyna0n9l2-Siqf/view?usp=drive_link">slides part 2</a>)
</td>
<td>
<span class="label label-green"><strong>Final Project Kickoff</strong></span>
</td>
<td>Surya, Aakarsh</td>
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<td style="max-width: 30px;">8</td>
<td>Apr 6</td>
<td>Reinforcement Learning
(<a href="https://docs.google.com/presentation/d/1gCrIlxWk25pgN173YMHSzU29HJT8txwiWZJxMP-pDtw/edit?usp=sharing">slides</a>)
</td>
<td>
<span class="label label-green"><strong>Final Project Proposal Due</strong></span>
<br>
<span class="label label-yellow"><strong>Homework 5</strong> (due April 13)</span>
<a href="https://colab.research.google.com/drive/1FPC6aq6oye9VMVNYb837pCwj3Bv9zsBy?usp=sharing">Diffusion</a> OR <a href="https://rail.eecs.berkeley.edu/deeprlcourse/static/homeworks/hw2.pdf">RL (Credit: CS 185)</a>
</td>
<td>Kavish, Tanmayi</td>
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<td style="max-width: 30px;">9</td>
<td>Apr 13</td>
<td>3D Computer Vision + World Models
(<a href="https://docs.google.com/presentation/d/17q41It5aBOr76ElDfqBNXHgcXvEfE5IWE2ZUhHqfekQ/edit?usp=sharing">slides</a>)
</td>
<td></td>
<td>Tim, Andrew, Rishi</td>
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<td style="max-width: 30px;">10</td>
<td>Apr 20</td>
<td>AI Safety + Conclusion
(<a href="https://docs.google.com/presentation/d/1xJsR3sMlBIUOhYuq03v-pV-P6RM_QQEm8OWBhl4c4Yw/edit?usp=sharing">slides</a>)
</td>
<td><span class="label label-green"><strong>Final Project Checkpoint Due</strong></span></td>
<td>Prakrat</td>
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<td style="max-width: 30px;">11</td>
<td>May 1</td>
<td><strong>Final Project Showcase</strong></td>
<td><span class="label label-green"><strong>Final Project Due</strong></span></td>
<td></td>
</tr>
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