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BE5B33KUI	Cybernetics and Artificial Intelligence
Role:	PV	Rozsah výuky:	2P+2C
Katedra:	13133	Jazyk výuky:	EN
Garanti:	Svoboda T.	Zakončení:	Z,ZK
Přednášející:	Pošík P., Svoboda T.	Kreditů:	6
Cvičící:	Osob je mnoho	Semestr:	L

Webová stránka:

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

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.

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.

Osnovy přednášek:

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.

Osnovy cvičení:

Computer lab organization. Search. Informed search and heuristics. Sequential decision problems. Reinforcement learning. Pattern Recognition.

Literatura:

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

Požadavky:

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

Poznámka:

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

Klíčová slova:

Cybernetics, artificial intelligence

Předmět je zahrnut do těchto studijních plánů:

Plán	Obor	Role	Dop. semestr
BEECS	Před zařazením do oboru	PV	4
BPEECS_2018	Před zařazením do oboru	PV	4

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Stránka vytvořena 23.11.2024 12:51:09, semestry: L/2023-4, Z/2024-5, Z/2025-6, L/2024-5, připomínky k informační náplni zasílejte správci studijních plánů

Návrh a realizace: I. Halaška (K336), J. Novák (K336)