====== 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 LLMs - [[https://colab.research.google.com/drive/1m9UXEiSO6cjVhhKFWlk-Yc7GVjh3dhhs?usp=sharing]]

7.	 	Interpretability in ML - [[https://colab.research.google.com/drive/1bSh4in8g792xBkYVpM8xQLOEO0XrlWjW?usp=sharing]]

8.	 	Markov decision processes - {{ :courses:becm36mlm:tutorials:tutorial_8_exercises.pdf | MP and MRP exercises}}

9.	 	Tabular RL I. - {{ :courses:becm36mlm:tutorials:mlm_tutorial_9_exercises.pdf | MDP exercises}}

10.	 	Tabular RL II.  [[https://github.com/supertweety/Minesweeper/blob/main/minesweeper_tabular_rl_exercise.ipynb]]

11.	 	Deep RL: Deep Q-learning. Policy gradient. [[https://github.com/supertweety/Minesweeper/blob/main/minesweeper_deep_rl_exercise.ipynb]]

12.	 	A/B tests and multi-armed bandit problems, UCB algorithm. Bayesian bandits (Thompson sampling). Contextual bandits.

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

14.	 	Intro to “Pearl’s” causality