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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2021/2022

Cybernetics and Artificial Intelligence

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Code Completion Credits Range Language
B3B33KUI Z,ZK 6 2P+2C Czech
Corequisite:
The course cannot be taken simultaneously with:
Cybernetics and Artificial Intelligence (AE3B33KUI)
Cybernetics and Artificial Intelligence (A3B33KUI)
Cybernetics and Artificial Intelligence (BE5B33KUI)
The course is a substitute for:
Cybernetics and Artificial Intelligence (A3B33KUI)
Lecturer:
Tomáš Svoboda (guarantor), Petr Pošík
Tutor:
Tomáš Svoboda (guarantor), Matěj Hoffmann, Jana Kostlivá, Martin Pecka, Petr Pošík, Zdeněk Straka, Petr Švarný
Supervisor:
Department of Cybernetics
Synopsis:

The course introduces the students into the field of artificial intelligence and gives the necessary basis for designing machine control algorithms. It advances the knowledge of state space search algorithms by including uncertainty in state transition. Students are introduced into reinforcement learning for solving problems when the state transitions are unknown, which also connects the artificial intelligence and cybernetics fields. Bayesian decision task introduces supervised learning. Learning from data is demonstrated on a linear classifier. Students practice the algoritms in computer labs.

Requirements:

Basic knowledge of linear algebra and programming is assumed. Experience in Python and basics of probability is an advantage.

Syllabus of lectures:

What is artificial intelligence and what cybernetics.

Solving problems by search. State space.

Informed search, heuristics.

Games, adversarial search.

Making sequential decisions, Markov decision process.

Reinforcement learning.

Bayesian decision task.

Paramater estimation for probablistic models. Maximum likelihood.

Learning from examples. Linear classifier.

Empirical evaluation of classifiers ROC curves.

Unsupervised learning, clustering.

Syllabus of tutorials:

Computer lab organization.

Search.

Informed search and heuristics.

Sequential decision problems.

Reinforcement learning.

Pattern Recognition.

Study Objective:

The course introduces the students into the field of artificial intelligence and gives the necessary basis for designing machine control algorithms. It advances the knowledge of state space search algorithms by including uncertainty in state transition. Students are introduced into reinforcement learning for solving problems when the state transitions are unknown, which also connects the artificial intelligence and cybernetics fields. Bayesian decision task introduces supervised learning. Learning from data is demonstrated on a linear classifier. Students practice the algoritms in computer labs.

Study materials:

Stuart J. Russel and Peter Norvig. Artificial Intelligence, a Modern Approach, 3rd edition, 2010

Note:
Further information:
http://cw.fel.cvut.cz/wiki/courses/b3b33kui/start
Time-table for winter semester 2021/2022:
Time-table is not available yet
Time-table for summer semester 2021/2022:
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
roomKN:E-230
Straka Z.
12:45–14:15
(lecture parallel1
parallel nr.101)

Karlovo nám.
Laboratoř PC
roomKN:E-230
Straka Z.
14:30–16:00
(lecture parallel1
parallel nr.102)

Karlovo nám.
Laboratoř PC
Thu
roomKN:E-230
Kostlivá J.
12:45–14:15
(lecture parallel1
parallel nr.103)

Karlovo nám.
Laboratoř PC
roomKN:E-230
Kostlivá J.
14:30–16:00
(lecture parallel1
parallel nr.104)

Karlovo nám.
Laboratoř PC
roomKN:E-132
Pecka M.
16:15–17:45
(lecture parallel1
parallel nr.108)

Karlovo nám.
Laboratoř PC
roomKN:E-132
Pecka M.
14:30–16:00
(lecture parallel1
parallel nr.107)

Karlovo nám.
Laboratoř PC
Fri
roomKN:E-230
Kostlivá J.
09:15–10:45
(lecture parallel1
parallel nr.105)

Karlovo nám.
Laboratoř PC
roomKN:E-230
Pošík P.
11:00–12:30
(lecture parallel1
parallel nr.106)

Karlovo nám.
Laboratoř PC
The course is a part of the following study plans:
Data valid to 2022-08-14
For updated information see http://bilakniha.cvut.cz/en/predmet4674006.html