1. Learning from Tabular data - https://drive.google.com/file/d/1B9XhJTXlolqfgaUTuQ36ct9OrlBj9zjT/view?usp=sharing
2. Learning from Relational data - https://drive.google.com/file/d/1GkH7xaXgZVU-I4hV_5TdDE2os9OpNW-f/view?usp=sharing
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.