Course details
Artificial Intelligence
UIN Acad. year 2004/2005 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
- 39 hrs lectures
- 12 hrs pc labs
- 14 hrs projects
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.
Prerequisite knowledge and skills
There are no prerequisites
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
Syllabus of lectures
- Introduction, types of AI problems, solving problem methods (BFS, DFS, DLS, IDS)
- Solving problem methods, cont. (BS, UCS, Hill Climbing, Simulated annealing, Backtracking, Forward checking)
- Solving problem methods, cont. (GS, BestFS, A*, IDA, SMA, Heuristic repair)
- Solving problem methods, cont. (Problem decomposition, AND/OR graphs)
- Methods of game playing (minimax, alpha-beta, games with unpredictability)
- Logic and AI, resolution and it's application in problem solving
- Implementation of basic search algorithms in PROLOG
- Implementation of basic search algorithms in LISP
- Machine learning
- Fundamentals of pattern recognition theory
- Principles of computer vision
- Principles of natural language processing
- Principles of expert systems
Syllabus of computer exercises
Počítačová cvičení jsou dvouhodinová a začínají v polovině semestru:
- Řešení úloh (7. výukový / 13. kalendářní týden)
- Řešení úloh (8. výukový / 14. kalendářní týden)
- PROLOG (9. výukový / 15. kalendářní týden)
- PROLOG (10. výukový / 16. kalendářní týden)
- LISP (11. výukový / 17. kalendářní týden)
- LISP (12. výukový / 18. kalendářní týden)
Progress assessment
Study evaluation is based on marks obtained for specified items. Minimimum number of marks to pass is 50.
Controlled instruction
There are no checked study.