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BE4M36SMU	Symbolic Machine Learning
Roles:	PV, PO	Extent of teaching:	2P+2C
Department:	13136	Language of teaching:	EN
Guarantors:	Kuželka O.	Completion:	Z,ZK
Lecturers:	Kuželka O., Šír G., Železný F.	Credits:	6
Tutors:	Too many persons	Semester:	L

Web page:

https://cw.fel.cvut.cz/wiki/courses/smu/start

Anotation:

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.

Course outlines:

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

Exercises outline:

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

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

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.

Subject is included into these academic programs:

Program	Branch	Role	Recommended semester
MEBIO1_2018	Bioinformatics	PV	2
MEBIO4_2018	Signal Processing	PV	2
MEBIO2_2018	Medical Instrumentation	PV	2
MEBIO3_2018	Image Processing	PV	2
MEOI7_2018	Artificial Intelligence	PO	2
MEOI9_2018	Data Science	PO	2
MEOI8_2018	Bioinformatics	PO	2

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Page updated 21.11.2024 17:51:36, semester: L/2023-4, L/2024-5, Z/2025-6, Z/2024-5, Send comments about the content to the Administrators of the Academic Programs

Proposal and Realization: I. Halaška (K336), J. Novák (K336)