We put a tentative syllabus here to give you an idea what future may bring. This syllabus is subject to change as the course progresses.
| # | Day | Date | Topic | Assignment | Speakers |
|---|---|---|---|---|---|
| 1 | Tue | Jan 12 | Introduction | Form reading group | |
| 2 | Tue | Jan 19 | Deep learning | Read Lecun-90c, AlexNet | |
| 3 | Tue | Jan 26 | Adversarial ML (1) | Read Intriguing properties of neural networks, FGSM attack | |
| 4 | Tue | Feb 2 | Adversarial ML (2) | Read PGD attack, Obfuscated gradients not useful | |
| 5 | Tue | Feb 9 | Adversarial ML (3) | Read Unrestricted attack, Blackbox attack | |
| 6 | Tue | Feb 16 | Testing DL | Read DeepXplore, VeriVis | Guest: Kexin Pei |
| 7 | Tue | Feb 23 | Verifying DL (1) | Read Reluplex, DeepSafe | |
| 8 | Tue | Mar 2 | No class (Spring recess) | ||
| 9 | Tue | Mar 9 | Verifying DL (2) | Read Reluval, Neurify | Guest: Shiqi Wang |
| 10 | Tue | Mar 16 | Verifying DL (3) | Read AI2, Abstract domain | |
| 11 | Tue | Mar 23 | Robustness training | Read Stability training, Adversarial logit training | |
| 12 | Tue | Mar 30 | Robustness training (2) | Read Metrics learning for robustness, Multitask learning for robustness | Guest: Chengzhi Mao |
| 13 | Tue | Apr 6 | Robustness tradeoffs | Read Robustness vs accuracy, Adversarial examples are features | |
| 14 | Tue | Apr 13 | Mini-research conference | Present and demo your final project |