Tutorials

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 - https://drive.google.com/file/d/1PCKRrnyKJmdl-3_G3c0_9OVrfleUCD2A/view?usp=sharing

4. Relational Deep Learning - https://drive.google.com/file/d/14eKH7lS8fy9z3Iv5y74zmir65OBNzPVx/view?usp=sharing

5. Neural-Symbolic Learning - https://colab.research.google.com/drive/1j0I2RHhN_c5kjdH3vjh2-RJ1eG-grCGf?usp=sharing

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.