Summary of Study | Summary of Branches | All Subject Groups | All Subjects | List of Roles | Explanatory Notes               Instructions

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AE3B33KUI	Cybernetics and Artificial Intelligence
Roles:	P, V	Extent of teaching:	2P+2C
Department:	13133	Language of teaching:	EN
Guarantors:		Completion:	Z,ZK
Lecturers:		Credits:	5
Tutors:		Semester:	L

Anotation:

The course will enable students to understand the basic concepts, goals and methods of cybernetics and artificial intelligence, and align some individual topics studied in the bachelor stage into the more profound context of the study program. The syllabus contains topics concerned with general aspects of systems and information theory, problem solving and state space search principles, elements of game theory, knowledge and expert systems, elements of decision theory, recognition and machine learning. The most important feature of the course is its unifying conceptual approach to many, at first sight diverse, components of cybernetics and aritifical intelligence.

Study targets:

The course will enable students to understand the basic concepts, goals and methods of cybernetics and artificial intelligence, and align some individual topics studied in the bachelor stage into the more profound context of the study program.

Course outlines:

Introduction to cybernetics, systems and models Elements of general systems theory Information, entropy, information transmission, coding - a cybernetic view Algorithmic entropy, decidability Problem solving, the resolution principle Search algorithms, stochastic search Game theory, two-player games Knowledge representation, semantic networks, production systems, frames and scenarios Expert systems, their architecture, uncertain information processing models Decision and classification principles, Bayesian decision making, attributes, attribute space, recognition, cluster analysis Structural recognition, relations to machine perception and image/scene analysis Neural networks and their training, genetic and evolutionary algorithms Machine learning Applications (if timetable allows)

Exercises outline:

1.	-	2.  Cybernetic systems lab showcase
2.	-	4.  Seminar: Probability and entropy
3.	-	4.  Computer lab: System models
5.	-	6.  Seminar: Information transmission
5.	-	6.  Computer lab: Compression algorithms
6.	-	10.  Seminar: Search
7.	-	10.  Computer lab: Search
8.	-	12.  Seminar: Decision making, classification, recognition
9.	-	12.  Computer lab: Expert systems
10.	-	13.  Seminar with computer simulation: Evolutionary algorithms, neural networks
11.	-	14.  Machine learning, class credits

Literature:

Nilsson, N. N.: Artificial Intelligence: A New Synthesis. Morgan Kaufmann Publ. San Francisco, 1998

Requirements:

https://cw.felk.cvut.cz/doku.php/courses/a3b33kui/start

Webpage:

http://cw.felk.cvut.cz/doku.php/courses/ae3b33kui/start

Keywords:

Cybernetics, artificial intelligence

Subject is included into these academic programs:

Program	Branch	Role	Recommended semester
BEKME1	Communication Technology	V	4
BEKME5	Komunikace a elektronika	V	4
BEKME_BO	Common courses	V	4
BEKME4	Network and Information Technology	V	4
BEKME3	Applied Electronics	V	4
BEKME2	Multimedia Technology	V	4
BEKYR1	Robotics	P	2
BEKYR_BO	Common courses	P	2
BEKYR3	Systems and Control	P	2
BEKYR2	Sensors and Instrumentation	P	2
BEEEM1	Applied Electrical Engineering	V	4
BEEEM_BO	Common courses	V	4
BEEEM2	Electrical Engineering and Management	V	4
BEOI1	Computer Systems	V	4
BEOI_BO	Common courses	V	4
BEOI3	Software Systems	V	4
BEOI2	Computer and Information Science	V	4

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Page updated 5.3.2021 17:52:09, semester: Z/2020-1, L/2021-2, L/2020-1, Z/2021-2, Send comments about the content to the Administrators of the Academic Programs

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