Lectures

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

1. Learning from Tabular data: slides

2. Learning from Relational data: slides

3. Graph Representation Learning: slides

4. Relational Deep Learning: slides

5. Neural-Symbolic Learning: slides

6. Learning with LLMs: slides

7. Interpretability in ML: slides

8. Markov decision processes: slides

9. Tabular RL I. slides

10. Tabular RL II. (We will continue with the slides from week 9).

11. Deep RL: Deep Q-learning. Policy gradient. Intro to Bandits: A/B tests and multi-armed bandit problems, UCB algorithm. Deep RL slides Bandits slides

12. Bayesian bandits (Thompson sampling). Contextual bandits. Policy gradient (finishing RL from Week 12). Thompson Sampling slides Deep RL slides

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

14. Intro to “Pearl’s” causality