Course details
Artificial Intelligence
UIN Acad. year 2003/2004 Summer semester 6 credits
Problem solving, state space search, problem decomposition, games playing. Knowledge representation. AI languages (PROLOG, LISP). Machine learning principles. Statistical and structural pattern recognition. Fundamentals of computer vision. Basic principles of natural language processing. Basic principles of expert systems.
Guarantor
Language of instruction
Completion
Time span
Department
Subject specific learning outcomes and competences
Students acquire knowledge of various approaches of problem solving and basic information about machine learning, computer vision, natural language processing and expert systems. They will be able to create programs using heuristics for problem solving.
Learning objectives
To give the students the knowledge of fundamentals of artificial intelligence, namely knowledge of problem solving approaches, machine learning principles and general theory of recognitions. Students acquire base information about computer vision, natural language processing and expert systems.
Study literature
- Zbořil,F., Hanáček,P.: Umělá inteligence, Skripta VUT v Brně, VUT v Brně, 1990, ISBN 80-214-0349-7
- Mařík,V., Štěpánková,O., Lažanský,J. a kol.: Umělá inteligence (1)+(2), ACADEMIA Praha, 1993 (1), 1997 (2), ISBN 80-200-0502-1
Fundamental literature
- Russel,S., Norvig.,P.: Artificial Intelligence, Prentice-Hall, Inc., 1995, ISBN 0-13-360124-2, second edition 2003, ISBN 0-13-080302-2
- Luger,G.F., Stubblefield,W.A.: Artificial Intelligence, The Benjamin/Cummings Publishing Company, Inc., 1993, ISBN 0-8053-4785-2
Progress assessment
Written mid-term exam
Course inclusion in study plans
- Programme EI-BC-3, field VTB, 1st year of study, Elective
- Programme EI-BC-3 (in English), field VTB, 1st year of study, Elective
- Programme EI-MGR-3, field VTN, 2nd year of study, Compulsory
- Programme EI-MGR-5, field VTI, 2nd year of study, Compulsory
- Programme EI-MGR-5 (in English), field VTI, 2nd year of study, Compulsory