Popis předmětu - BE5B33KUI

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BE5B33KUI Cybernetics and Artificial Intelligence Rozsah výuky:2+2c
Garanti:Svoboda T. Role:PV Jazyk výuky:EN
Vyučující:Hoffmann M., Svoboda T. Zakončení:Z,ZK
Zodpovědná katedra:13133 Kreditů:6 Semestr:L

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 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.

Poznámka:

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

Webová stránka:

https://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


Stránka vytvořena 15.10.2018 14:48:09, semestry: Z,L/2020-1, L/2017-8, L/2019-20, Z,L/2018-9, Z/2019-20, připomínky k informační náplni zasílejte správci studijních plánů Návrh a realizace: I. Halaška (K336), J. Novák (K336)
Za obsah odpovídá: doc. Ing. Ivan Jelínek, CSc.