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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2023/2024

Cybernetics and Artificial Intelligence

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Code Completion Credits Range Language
B3B33KUI Z,ZK 6 2P+2C Czech
Vztahy:
It is not possible to register for the course B3B33KUI if the student is concurrently registered for or has already completed the course BE5B33KUI (mutually exclusive courses).
During a review of study plans, the course A3B33KUI can be substituted for the course B3B33KUI.
It is not possible to register for the course B3B33KUI if the student is concurrently registered for or has already completed the course AE3B33KUI (mutually exclusive courses).
It is not possible to register for the course B3B33KUI if the student is concurrently registered for or has already completed the course A3B33KUI (mutually exclusive courses).
In order to register for the course B3B33KUI, the student must have registered for the required number of courses in the group BEZBM no later than in the same semester.
It is not possible to register for the course B3B33KUI if the student is concurrently registered for or has previously completed the course BE5B33KUI (mutually exclusive courses).
The requirement for course B3B33KUI can be fulfilled by substitution with the course BE5B33KUI.
It is not possible to register for the course B3B33KUI if the student is concurrently registered for or has previously completed the course A3B33KUI (mutually exclusive courses).
It is not possible to register for the course B3B33KUI if the student is concurrently registered for or has previously completed the course AE3B33KUI (mutually exclusive courses).
Garant předmětu:
Tomáš Svoboda
Lecturer:
Petr Pošík, Tomáš Svoboda
Tutor:
Jana Kostlivá, Petr Pošík, Tomáš Svoboda, Pavel Šindler
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.

Learning from examples. Linear classifier. Nearest neighbors method.

Empirical evaluation of classifiers ROC curves.

Syllabus of tutorials:

During computer labs and at home, students will implement several algorithms introduced at lectures. The emphasis will be put to testing the functionality of their implementation. When exercising classification problems, we will also discuss the topics training and testing data, crossvalidation and ROC curve. A technical report will be required for some of the tasks.

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

Richard O. Duda, Peter E. Hart, David G. Stork. Pattern Classification, 2nd edition. 2000

Christopher M. Bishop. Pattern Recognition and Machine Learning. 2006

Note:
Further information:
http://cw.fel.cvut.cz/wiki/courses/b3b33kui/start
Time-table for winter semester 2023/2024:
Time-table is not available yet
Time-table for summer semester 2023/2024:
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
roomKN:E-230
Pošík P.
12:45–14:15
(lecture parallel1
parallel nr.101)

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

Karlovo nám.
Laboratoř PC
roomKN:E-230
Šindler P.
16:15–17:45
(lecture parallel1
parallel nr.105)

Karlovo nám.
Laboratoř PC
Fri
roomKN:E-230
Kostlivá J.
09:15–10:45
(lecture parallel1
parallel nr.103)

Karlovo nám.
Laboratoř PC
roomKN:E-230
Kostlivá J.
11:00–12:30
(lecture parallel1
parallel nr.104)

Karlovo nám.
Laboratoř PC
roomKN:E-230
Šindler P.
12:45–14:15
(lecture parallel1
parallel nr.106)

Karlovo nám.
Laboratoř PC
The course is a part of the following study plans:
Data valid to 2024-05-25
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet4674006.html