====== 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

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.