Symbolic Machine Learning
| Code | Completion | Credits | Range | Language |
|---|---|---|---|---|
| BE4M36SMU | Z,ZK | 6 | 2P+2C | English |
- Relations:
- During a review of study plans, the course B4M36SMU can be substituted for the course BE4M36SMU.
- It is not possible to register for the course BE4M36SMU if the student is concurrently registered for or has already completed the course B4M36SMU (mutually exclusive courses).
- It is not possible to register for the course BE4M36SMU if the student is concurrently registered for or has previously completed the course B4M36SMU (mutually exclusive courses).
- Course guarantor:
- Ondřej Kuželka
- Lecturer:
- Ondřej Kuželka, Gustav Šír, Filip Železný
- Tutor:
- Ondřej Kuželka, Petr Ryšavý, Martin Svatoš, Gustav Šír, Jan Tóth, Filip Železný
- Supervisor:
- Department of Computer Science
- Synopsis:
-
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.
- Requirements:
-
Students can get a maximum of 100 points which is the sum of the projects score and the exam score.
A minimum of 25 (out of 50) exam points is required to pass the exam.
A minimum of 25 (out of 50) projects points is required to obtain an assessment.
- Syllabus of lectures:
-
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
- Syllabus of tutorials:
-
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
- Study Objective:
- Study materials:
-
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
- Note:
- Further information:
- https://cw.fel.cvut.cz/wiki/courses/smu/start
- Time-table for winter semester 2024/2025:
- Time-table is not available yet
- Time-table for summer semester 2024/2025:
-
06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00
Mon Tue Wed Thu Fri - The course is a part of the following study plans:
-
- Open Informatics - Artificial Intelligence (compulsory course of the specialization)
- Open Informatics - Bioinformatics (compulsory course of the specialization)
- Open Informatics - Data Science (compulsory course of the specialization)
- Medical Electronics and Bioinformatics - Specialization Image Processing (compulsory elective course)
- Medical Electronics and Bioinformatics - Specialization Signal Processing (compulsory elective course)
- Medical Electronics and Bioinformatics - Specialization Bioinformatics (compulsory elective course)
- Medical Electronics and Bioinformatics - Specialization Medical Instrumentation (compulsory elective course)