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mlberkeley · Sep 24, 2024 · f7dcebf · f7dcebf
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diff --git a/docs/policies.md b/docs/policies.md
@@ -5,17 +5,17 @@ layout: home
 ---
 
 
-#### Lectures
+### Lectures
 Lectures are on Mondays, 8-10 pm @ AAPB 115, except common midterms (CS61ABC, 70, etc.) and holidays.
 
-#### Attendance
+### Attendance
 Attendance is mandatory. If you absolutely cannot make it to lecture, please DM Tim or Tejas.
 
-#### Homework
+### Homework
 Throughout the semester, we'll be releasing homework that accompanies lectures. To subvert limitless extensions, we'll be implementing a hard extension policy requiring you to turn in homework **at most 4 days after the deadline**. This policy is for your benefit and will help you keep pace with the course!
 
 We'll also be hosting weekly **homework parties** so you can ask questions and work with your fellow new members on the homework. (Details for times/locations for these will be released soon).
 
-#### Projects
+### Projects
 Towards the latter half of NMEP, you'll also be completing a project of your choosing with the help of mentors (older ML@B members). Project teams will be **3-4 people**, and there will be a final project showcase at the end of the semester so you can talk about the ✨cool✨ things you built/discovered!
 
diff --git a/docs/resources.md b/docs/resources.md
@@ -4,18 +4,18 @@ nav_order: 3
 layout: home
 ---
 
-#### HW 0 (Math)
+### HW 0 (Math)
 - [Princeton NEU 314](http://pillowlab.princeton.edu/teaching/mathtools16/slides/lec10_LeastSquaresRegression.pdf) Sections 1 through 2.1 (Least squares with vector calculus)
 - [Stanford cheat sheets](https://stanford.edu/~shervine/teaching/) on ML and math
 - [Calculus for ML](https://ml-cheatsheet.readthedocs.io/en/latest/calculus.html) cheat sheet
 
-#### Neural Networks
+### Neural Networks
 - [CS197 Harvard](https://docs.google.com/document/d/1dA8KmOTZePMRl3MixxM6Fb0H8IJhIyn_g-LUXbRHeqU): Moonwalking with PyTorch
 
-#### Computer Vision
+### Computer Vision
 - JPEG through the lens of singal processing ([YouTube](https://www.youtube.com/watch?v=0me3guauqOU))
 - [Understanding Convolutions](https://drive.google.com/drive/folders/1e4C7s3pEPt2lLirIu02DiUdKlKlvsMo5?usp=sharing) (old NMEP HW!)
 
-#### Papers
+### Papers
 - [How to read a paper](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf)
 - [Visualizing loss landscape of neural nets](https://arxiv.org/pdf/1712.09913.pdf)
diff --git a/index.md b/index.md
@@ -11,25 +11,25 @@ In the New Member Education Program (NMEP), we'll be paving a sturdy ML foundati
 
 ### Schedule
 
-| Week | Date          | Lecture | Assignment | Lecturer |
+| Week | Date  | Lecture                           | Assignments                                 | Lecturer                |
 |:-------------|:------------------|:------| :-| :-|
-| 1  | Sep 23 | Intro + Background Review          | | |
-| 2  | Sep 30 | Classical ML                       | | |
-| 3  | Oct 7  | Deep Learning, Embeddings          | | |
-| 4  | Oct 14 | CNNs & Object Detection            | | |
-| 5  | Oct 21 | NLP & Transformers 1               | | |
-| 6  | Oct 28 | Transformers 2                     | | |
-| 7  | Nov 4  | Self-supervised Learning           | | |
-| 8  | Nov 11 | RL                                 | | |
-| 9  | Nov 18 | GANs, Diffusion                    | | |
-| 10 | Nov 25 | Ethics                             | | |
-| 11 | Dec 2  | Special Topics                     | | |
-| 12 | Dec 9  | (No Lecture) Final Presentations   | | |
-
+| 1  | Sep 23 | Intro + Background Review          | Lecture Exercise {: .label} [Rockfall] Homework 1 {: .label .label-yellow} Math Review                 |                         |
+| 2  | Sep 30 | Classical ML                       |                                             |                         |
+| 3  | Oct 7  | Deep Learning, Embeddings          |                                             |                         |
+| 4  | Oct 14 | CNNs & Object Detection            |                                             |                         |
+| 5  | Oct 21 | NLP & Transformers 1               |                                             |                         |
+| 6  | Oct 28 | Transformers 2                     |                                             |                         |
+| 7  | Nov 4  | Self-supervised Learning           |                                             |                         |
+| 8  | Nov 11 | RL                                 |                                             |                         |
+| 9  | Nov 18 | GANs, Diffusion                    |                                             |                         |
+| 10 | Nov 25 | Ethics                             |                                             |                         |
+| 11 | Dec 2  | Special Topics                     |                                             |                         |
+| 12 | Dec 9  | (No Lecture) Final Presentations   |                                             |                         |
 
 
 
 
+[Rockfall]: https://drive.google.com/file/d/1UM2w5BrJrEcI69_eqhIn-t1yrdC7XzY9/view?usp=drive_link
 [Just the Docs]: https://just-the-docs.github.io/just-the-docs/
 [GitHub Pages]: https://docs.github.com/en/pages
 [README]: https://github.com/just-the-docs/just-the-docs-template/blob/main/README.md