Current quarter's videos are available through SCPD.
Course notes are published here.
| Week | Date | Lecture Topics | Coursework | Sections |
|---|---|---|---|---|
| 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 | Guest Lecture (Antonio Vergari): Probabilistic Circuits: Representations, Inference, Learning and Applications (slides) | HW 3 due | |
| 12 | Dec 4 |
Guest Lecture (Kristy Choi): Meta-Amortized Variational Inference and Learning (slides), Guest Lecture (Rui Shu): Weakly Supervised Disentanglement with Guarantees (slides) |
||
| Poster Presentation: Day: December 6, 2019 - Time: 10:00 AM to 4:00 PM - Location: McCaw Hall @ Arrillaga. | ||||
| 13 | Dec 9 | Finals Week | ||
| Final Project Reports: Due December 11, 2019. | ||||