Link: https://www.sri.inf.ethz.ch/teaching/riai2020
Video: https://www.youtube.com/playlist?list=PLWjm4hHpaNg6c-W7JjNYDEC_kJK9oSp0Y

OVERVIEW

Creating reliable and explainable probabilistic models is a fundamental challenge to solving the artificial intelligence problem. This course covers some of the latest and most exciting advances that bring us closer to constructing such models. The main objective of this course is to expose students to the latest and most exciting research in the area of explainable and interpretable artificial intelligence, a topic of fundamental and increasing importance. Upon completion of the course, the students should have mastered the underlying methods and be able to apply them to a variety of problems. To facilitate deeper understanding, an important part of the course will be a group hands-on programming project where students will build a system based on the learned material.

The course covers some of the latest research (over the last 2-3 years) underlying the creation of safe, trustworthy, and reliable AI:

Adversarial Attacks on Deep Learning (noise-based, geometry attacks, sound attacks, physical attacks, autonomous driving, out-ofppt-distribution)
Defenses against attacks
Combining gradient-based optimization with logic for encoding background knowledge
Complete Certification of deep neural networks via automated reasoning (e.g., via numerical abstractions, mixed-integer solvers)
Probabilistic certification of deep neural networks
Training deep neural networks to be provably robust via automated reasoning
Understanding and Interpreting Deep Networks
Probabilistic Programming

Lectures

1. Introduction
2. Adversarial attacks I
3. Adversarial attacks II
4. Adversarial Defenses and Certification of Neural Networks
5. Certification with complete methods
6. The Zonotope convex relaxation
7. DeepPoly Relaxation
8. Project introduction
9. Certified Defenses
10. Geometric Robustness
11. Network Interpretability and Visualization
12. Combining Logic and Deep Learning
13. Randomized Smoothing for Robustness
14. Summary and future directions