ČESKÉ VYSOKÉ UČENÍ TECHNICKÉ V PRAZE
STUDIJNÍ PLÁNY
2020/2021

# Cybernetics and Artificial Intelligence

Kód Zakončení Kredity Rozsah Jazyk výuky
BE5B33KUI Z,ZK 6 2P+2C anglicky
Korekvizita:
Předmět nesmí být zapsán současně s:
Cybernetics and Artificial Intelligence (AE3B33KUI)
Kybernetika a umělá inteligence (A3B33KUI)
Kybernetika a umělá inteligence (B3B33KUI)
Předmět je náhradou za:
Cybernetics and Artificial Intelligence (AE3B33KUI)
Kybernetika a umělá inteligence (A3B33KUI)
Kybernetika a umělá inteligence (B3B33KUI)
Přednášející:
Tomáš Svoboda (gar.), Matěj Hoffmann
Cvičící:
Tomáš Svoboda (gar.), Filipe Gama, Jana Kostlivá
Předmět zajišťuje:
katedra kybernetiky
Anotace:

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.

Basic knowledge of probability and linear algebra is assumed. We expected student is able to write decent computer programs in a higher level language (Java, Python), and have basic knowledge about data structures. Python will be used in computer labs.

Osnova přednášek:

What is artificial intelligence and what cybernetics.

Solving problems by search. State space.

Informed search, heuristics.

Making sequential decisions, Markov decision process.

Reinforcement learning.

Paramater estimation for probablistic models. Maximum likelihood.

Learning from examples. Linear classifier.

Empirical evaluation of classifiers ROC curves.

Unsupervised learning, clustering.

Osnova cvičení:

Computer lab organization.

Search.

Informed search and heuristics.

Sequential decision problems.

Reinforcement learning.

Pattern Recognition.

Cíle studia:

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.

Studijní materiály:

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

Poznámka:

http://cw.fel.cvut.cz/wiki/courses/be5b33kui/start

Další informace:
https://cw.fel.cvut.cz/wiki/courses/be5b33kui/start
Rozvrh na zimní semestr 2020/2021:
Rozvrh není připraven
Rozvrh na letní semestr 2020/2021:
 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 místnost KN:E-112Hoffmann M.Svoboda T.10:00–11:45(přednášková par. 1)Karlovo nám.Cvičebna Vyčichlovamístnost KN:E-132Gama F.12:45–14:15(přednášková par. 1paralelka 101)Karlovo nám.Laboratoř PC
Předmět je součástí následujících studijních plánů:
Platnost dat k 14. 6. 2021
Aktualizace výše uvedených informací naleznete na adrese http://bilakniha.cvut.cz/cs/predmet4358106.html