Detailed Syllabus


Current quarter's videos are available through SCPD.
Course notes are published here.

WeekDateLecture TopicsCourseworkSections
1 Sep 23 Introduction and Background
(slides 1, slides 2)
Probability and Linear Algebra
2 Sep 30 Autoregressive Models
(slides 3, slides 4)
HW 1 released PyTorch
3 Oct 7 Variational Autoencoders (slides 5, slides 6) CNNs, RNNs, Transformers
4 Oct 14 Normalizing Flow Models (slides 7, slides 8) HW1 due (10/15), HW 2 released
5 Oct 21 Generative Adversarial Networks (slides 9, slides 10)
Project Proposal: Due Monday, October 21, 2019.
6 Oct 28 Energy-based Models (slides 11)
Guest Lecture (Yang Song): Gradient Estimation for Generative Modeling (slides)
HW 2 due (10/29)
7 Nov 4 Combining Generative Model Variants (slides 12)
Midterm: Day: November 4, 2019 - Time: 6:00 PM to 9:00 PM - Location: TBA.
8 Nov 11 Evaluation of Generative Models, GAIL: Generative Adversarial Imitation Learning (slides 13, slides 14) Project Progress Report due (11/18), HW 3 released
9 Nov 18 Discreteness in Latent Variable Modeling (slides 15)
10 Nov 25 Thanksgiving Break
11 Dec 2 Applications: Vision, Speech, Language, Graphs, Reinforcement Learning HW 3 due
Poster Presentation: Day: December 6, 2019 - Time: 10:00 AM to 4:00 PM - Location: McCaw Hall @ Arrillaga.
12 Dec 9 Finals Week
Final Project Reports: Due December 11, 2019.

Additional Reading: Surveys and Tutorials


  1. Tutorial on Deep Generative Models. Aditya Grover and Stefano Ermon. International Joint Conference on Artificial Intelligence, July 2018.
  2. Tutorial on Generative Adversarial Networks. Computer Vision and Pattern Recognition, June 2018.
  3. Tutorial on Deep Generative Models. Shakir Mohamed and Danilo Rezende. Uncertainty in Artificial Intelligence, July 2017.
  4. Tutorial on Generative Adversarial Networks. Ian Goodfellow. Neural Information Processing Systems, December 2016.
  5. Learning deep generative models. Ruslan Salakhutdinov. Annual Review of Statistics and Its Application, April 2015.