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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
BE5B33KUI Z,ZK 6 2P+2C English
Corequisite:
The course cannot be taken simultaneously with:
Cybernetics and Artificial Intelligence (AE3B33KUI)
Cybernetics and Artificial Intelligence (A3B33KUI)
Cybernetics and Artificial Intelligence (B3B33KUI)
The course is a substitute for:
Cybernetics and Artificial Intelligence (AE3B33KUI)
Cybernetics and Artificial Intelligence (A3B33KUI)
Cybernetics and Artificial Intelligence (B3B33KUI)
Lecturer:
Tomáš Svoboda (guarantor), Petr Pošík
Tutor:
Tomáš Svoboda (guarantor), Filipe Gama, Matěj Hoffmann, Jana Kostlivá, Petr Pošík
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:
https://cw.fel.cvut.cz/wiki/courses/be5b33kui/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
roomKN:E-127
Pošík P.
Svoboda T.

11:00–12:30
(lecture parallel1)
Karlovo nám.
Kotkova cvičebna K4
roomKN:E-132
Gama F.
12:45–14:15
(lecture parallel1
parallel nr.101)

Karlovo nám.
Laboratoř PC
Tue
Wed
Thu
Fri
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/predmet4358106.html