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.