Instructor: R. Teal Witter. Please call me Teal.
Meeting Times: We meet Monday, Tuesday, Wednesday, and Thursday in 75 Shannon Room 202. Lectures are from 10am to noon, code demonstrations from 2 to 3pm, and office hours from 3 to 4pm.
Participation: I expect you to engage in class, ask questions, and make connections. So that I can get a sense of how you’re doing, please fill out this form once per lecture. (You will receive one point per response.)
Discussion: Please post all your course related questions on Canvas. If your question reveals your solution to a homework problem, please email me instead.
Assignments: You will have one homework problem per class (generally due the next Friday) and a project on a topic of your choice.
I will update the homework before each class to reflect that day’s problem.
| Assignment | Work Due | Self-grade Due |
|---|---|---|
| Homework 1 (LaTeX) | Friday 1/13 | Monday 1/16 |
| Homework 2 (LaTeX) | Friday 1/20 | Monday 1/23 |
| Homework 3 (LaTeX) | Friday 1/27 | Monday 1/30 |
| Homework 4 (LaTeX) | Wednesday 2/1 | Friday 2/3 |
| Project Proposal | Monday 1/23 | |
| Project | Friday 2/3 |
Resources: This class uses material from Chinmay Hegde’s phenomenal graduate deep learning class at NYU Tandon. For each class, I will post my handwritten notes, the python notebook for the demo, and the written material I used to prepare. If you would like an additional resource, I have heard the free online textbook Dive into Deep Learning is excellent.
| Class | Topic | Material | Demo |
|---|---|---|---|
| Thursday 1/5 | Introduction | Reading / Notes | PyTorch basics |
| Monday 1/9 | Neural Networks | Reading 1 / Reading 2 / Notes | Autodiff |
| Tuesday 1/10 | Deep Networks | Reading / Notes | Optimization |
| Wednesday 1/11 | Convolutional Networks | Reading / Notes | Convolutions |
| Thursday 1/12 | Object Detection | Reading / Notes | Resnet |
| Tuesday 1/17 | Recurrent Networks | Reading / Notes | RNN |
| Wednesday 1/18 | Transformers | Reading / Notes | Transformers |
| Thursday 1/19 | Natural Language Processing | Reading / Notes | Word2Vec |
| Monday 1/23 | RL: Policy Gradients | Reading / Notes | Policy gradients |
| Tuesday 1/24 | RL: Q-Learning | Reading / Notes | Q-learning |
| Wednesday 1/25 | Generative Adversarial Networks | Reading / Notes | Conditional GAN |
| Thursday 1/26 | Contrastive Learning | Reading / Notes | CLIP |
| Monday 1/30 | Stable Diffusion | Reading / Notes | Diffusion |
| Tuesday 1/31 | Implicit Regularization | Reading / Notes | Regularization |
| Wednesday 2/1 | Project Preparation | ||
| Thursday 2/2 | Project Presentations |