Schedule

*This schedule is evolving and will change based on your interests and how the class is progressing. See here for a list of topics and related papers.
Lec. Date Topic Slides Notes
1
Sept. 5
 Introduction Lecture01
2
Sept. 12
 Neural Networks Lecture02 Indicate your preferred papers for in-class presentation due Sept. 12
3
Sept. 19
 Convolutional Neural Networks Lecture03
4
Sept. 26
 Recurrent Neural Networks Lecture04
5
Oct. 3
 Language Models, Transformers Lecture05
6
Oct. 10
 Vision Transformers Lecture06
7
Oct. 17
 Generative Adversarial Networks, Diffusion
Oct. 24
 Fall Break No Class
Oct 31
 No Class
8
Nov. 7
 Few-shot Learning, Zero-shot Learning
9
Nov. 14
 Self-supervised Learning, Deep Clustering
10
Nov. 21
 Interpretable AI
11
Nov. 28
 Final Project Presentations
12
Dec. 5
 Final Project Presentations
Project Reports due on December 10