Detailed Syllabus


Current quarter's videos are available through Bruin Learn/Canvas.
Course notes are published here.

WeekDateLecture TopicsCoursework
1 Jan 4, 6 Introduction [slides 1, 2]
2 Jan 11, 13 Autoregressive Models [slides 1, 2] HW 1 released Tue 11:59 PM
3 Jan 18, 20 Variational Autoencoders [slides 1, 2]
4 Jan 25, 27 Normalizing Flows [slides 1, 2] Quiz 1 (Tue-Thu), HW 1 due Tue 11:59 PM
5 Feb 1, 3 Generative Adversarial Networks [slides 1, 2] Project Proposal due Sun (Feb 6) 11:59 PM
6 Feb 8, 10 Evaluating generative models, In-class mid term on Feb 10 [slides 1]
7 Feb 15, 17 Energy-based models [slides 1, 2]
8 Feb 22, 24 Model combination [slides 1]
 Quiz 2 (Tue), Project Progress Report due Sun 11:59 PM
9 March 1, 3 Score-based generative models, diffusion models
10 Mar 8, 10 Guest Lecture, Final Project In-class Presentations
Final Project Reports: Due March 13, 2022, 11:59 PM
Peer Feedback: Due March 18, 2022, 11:59 PM

Additional Reading: Surveys and Tutorials


  1. Tutorial on Deep Generative Models. Aditya Grover. Deep Learning for Science Summer School, July 2020.
  2. How to Train Your Energy-Based Models. Yang Song and Diederik P. Kingma. February 2021.
  3. Tutorial on Deep Generative Models. Aditya Grover and Stefano Ermon. International Joint Conference on Artificial Intelligence, July 2018.
  4. Tutorial on Generative Adversarial Networks. Computer Vision and Pattern Recognition, June 2018.
  5. Tutorial on Deep Generative Models. Shakir Mohamed and Danilo Rezende. Uncertainty in Artificial Intelligence, July 2017.
  6. Tutorial on Generative Adversarial Networks. Ian Goodfellow. Neural Information Processing Systems, December 2016.
  7. Learning deep generative models. Ruslan Salakhutdinov. Annual Review of Statistics and Its Application, April 2015.