Lectures

meeting backup: https://meet.google.com/dnr-uxfi-yay

1. Learning from Tabular data: slides

2. Learning from Relational data: slides

3. Graph Neural Networks

4. Relational Deep Learning

5. Neural-Symbolic Learning

6. Learning with Large Language Models

7. Interpretability in ML

8. Potential outcomes - Rubin-Neyman causal model, uplift modeling

9. Intro to “Pearl’s” causality

10. A/B tests and multi-armed bandit problems, UCB algorithm.

11. Bayesian bandits (Thompson sampling). Contextual bandits.

12. Markov decision processes

13. Tabular RL: Q-Learning and SARSA

14. Deep RL: Deep Q-learning. Policy gradient.