Machine Learning Methods
Semestr: Summer
Range: 2P+2C
Completion:
Credits: 6
Programme type: Master
Study form: Fulltime
Course language: English
Summary:
Students will get familiar with advanced machine learning methods (MLM) that go beyond common data domains (vision, text) taught in the other courses (e.g., BE4M33MPV, BECM36NLPT). They will learn techniques that work well for tabular and structured data (e.g., relational databases), including rule/tree ensembles, graph neural networks, and other advanced approaches aimed at complex learning problems. Additionally, the course will also teach students methods for model interpretability, the basics of causality, and reinforcement learning.
Keywords:
Course syllabus:
1. Learning from Tabular data
2. Learning from Structured data
3. Graph Neural Networks
4. Relational Deep Learning
5. Neural-Symbolic Learning
6. Learning with Large Language Models
7. Interpretability of ML Models
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.
Seminar syllabus:
1. Learning from Tabular data
2. Learning from Structured data
3. Graph Neural Networks
4. Relational Deep Learning
5. Neural-Symbolic Learning
6. Learning with Large Language Models
7. Interpretability of ML Models
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.