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B3B33KUI	Cybernetics and Artificial Intelligence
Roles:	P, PV	Extent of teaching:	2P+2C
Department:	13133	Language of teaching:	CS
Guarantors:	Svoboda T.	Completion:	Z,ZK
Lecturers:	Pošík P., Svoboda T.	Credits:	6
Tutors:	Kostlivá J., Pošík P., Svoboda T., Šindler P.	Semester:	L

Web page:

http://cw.fel.cvut.cz/wiki/courses/b3b33kui/start

Anotation:

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 targets:

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.

Course outlines:

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.

Exercises outline:

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.

Literature:

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

Requirements:

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

Note:

http://cw.fel.cvut.cz/wiki/courses/b3b33kui/start

Keywords:

Cybernetics, artificial intelligence, machine learning

Subject is included into these academic programs:

Program	Branch	Role	Recommended semester
BPBIO_2018	Common courses	PV	–
BPKYR_2016	Common courses	P	4
BPKYR_2021	Common courses	P	2

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Page updated 24.4.2024 17:51:15, semester: Z/2024-5, Z,L/2023-4, Send comments about the content to the Administrators of the Academic Programs

Proposal and Realization: I. Halaška (K336), J. Novák (K336)