Cybernetics and Artificial Intelligence

B232 - Summer 23/24
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B172 - Summer 17/18 B182 - Summer 18/19 B202 - Summer 20/21 B212 - Summer 21/22 B222 - Summer 22/23 B232 - Summer 23/24

Cybernetics and Artificial Intelligence - B3B33KUI

Student survey results
Credits 6
Semesters Summer
Completion Assessment + Examination
Language of teaching Czech
Extent of teaching 2P+2C
Annotation
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 outlines
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.