Statistical Deep Learning (Fall 2024)

Course Schedule

Week	Date	Topics	Slides	Reading
1	9/3	Course Introduction	Slide
2	9/10	Review of Linear Models	Slide
3	9/17	No class (Mid-Autumn Festival)
4	9/24	Machine Learning Basics	Slide	DL Ch. 5
5	10/1	Multilayer Perceptron	Slide	D2L Ch. 5 & DL Ch. 6
6	10/8	Optimization for DL Models	Slide	D2L Ch. 12 & DL Ch. 8
7	10/15	Regularization for Deep Learning	Slide	DL Ch. 7
8	10/22	Project Proposal
9	10/29	Implementation of DL Models	Slide Colab	D2L Ch. 6
10	11/5	Convolutional Networks	Slide	D2L Ch. 7, 8 & DL Ch. 9
11	11/12	Recurrent Networks	Slide	D2L Ch. 9, 10 & DL Ch. 10
12	11/19	Generative Models: Autoencoder	Slide	DL Ch. 13, 14
13	11/26	Generative Models: GAN, Flow-based models, Diffusion models	Slide
14	12/3	Attention Mechanism and Graph Neural Networks	Slide
15-16	12/10-17	Final Project Presentation

Important Dates:

• 9/17: No Class (Mid-Autumn Festival)
• 10/22: Proposal Presentation
• 12/10-17: Final Project Presentation