Machine Learning Methods

Course guarantor:

Filip Železný

Lecturer:

Ondřej Kuželka, Gustav Šír, Filip Železný

Tutor:

Ondřej Kuželka, Gustav Šír

Supervisor:

Department of Computer Science

Synopsis:

Students will get familiar with machine learning methods that go beyond the standard settings taught in basic ML courses. They will learn methods that work well for tabular and structured data domains (e.g. relational databases), including graph neural networks and recent neuro-symbolic techniques. The course will also teach the students some methods for model interpretability, basics of causality, and reinforcement learning.

Requirements:

Syllabus of lectures:

1. Learning from Tabular data

2. Ensembling and boosting

3. Learning from Structured data

4. Graph Neural Networks

5. Neural-Symbolic methods

6. ML Interpretability

7. ML Operations

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

9. Intro to Pearls 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.

Syllabus of tutorials:

Study Objective:

Study materials:

Note:

Time-table for winter semester 2025/2026:

Time-table is not available yet

Time-table for summer semester 2025/2026:

Time-table is not available yet

The course is a part of the following study plans:

• prg.ai Master (compulsory course in the program)