Description:

This course consists of four parts. The first part of the course will explain methods through which an intelligent agent can learn by interacting with its environment, also known as reinforcement learning. This will include deep reinforcement learning. The second part focuses on Bayesian networks, specifically methods for inference. The third part will cover fundamental topics from natural language learning, starting from the basics and ending with state-of-the-art architectures such as transformer. Finally, the last part will provide an introduction to several topics from the computational learning theory, including the online and batch learning settings.

Contents:

1. Reinforcement Learning - Markov decision processes
2. Reinforcement Learning - Model-free policy evaluation
3. Reinforcement Learning - Model-free control
4. Reinforcement Learning - Deep reinforcement learning
5. Bayesian Networks - Intro
6. Bayesian Networks - Variable elimination, importance sampling
7. Natural Language Processing 1
8. Natural Language Processing 2
9. Natural Language Processing 3
10. Natural Language Processing 4
11. Computational Leaning Theory 1
12. Computation Learning Theory 2
13. Computational Learning Theory 3.
14. Course Wrap Up

Seminar contents:

1. Reinforcement Learning - Markov decision processes
2. Reinforcement Learning - Model-free policy evaluation
3. Reinforcement Learning - Model-free control
4. Reinforcement Learning - Deep reinforcement learning
5. Bayesian Networks - Intro
6. Bayesian Networks - Variable elimination, importance sampling
7. Natural Language Processing 1
8. Natural Language Processing 2
9. Natural Language Processing 3
10. Natural Language Processing 4
11. Computational Leaning Theory 1
12. Computation Learning Theory 2
13. Computational Learning Theory 3.
14. Course Wrap Up

Recommended literature:

R. S. Sutton, A. G. Barto: Reinforcement learning: An introduction. MIT press, 2018.
D. Jurafsky & J. H. Martin: Speech and Language Processing - 3rd edition draft
M. J. Kearns, U. Vazirani: An Introduction to Computational Learning Theory, MIT Press 1994

Abbreviations used:

Semester:

• W ... winter semester (usually October - February)
• S ... spring semester (usually March - June)
• W,S ... both semesters

Mode of completion of the course:

• A ... Assessment (no grade is given to this course but credits are awarded. You will receive only P (Passed) of F (Failed) and number of credits)
• GA ... Graded Assessment (a grade is awarded for this course)
• EX ... Examination (a grade is awarded for this course)
• A, EX ... Examination (the award of Assessment is a precondition for taking the Examination in the given subject, a grade is awarded for this course)

Weekly load (hours per week):

• P ... lecture
• C ... seminar
• L ... laboratory
• R ... proseminar
• S ... seminar