Faculty of Information Technology, BUT

Course details

Artificial Intelligence

UIN Acad. year 2005/2006 Summer semester 6 credits

Current academic year

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.


Language of instruction

Czech, English


Examination (written)

Time span

39 hrs lectures, 12 hrs pc labs, 14 hrs projects

Assessment points

60 exam, 20 half-term test, 20 projects




Jurka Pavel, Ing. (DITS FIT BUT)
Mazal Zdeněk, Ing. (DITS FIT BUT)
Zbořil František, doc. Ing., Ph.D. (DITS FIT BUT)

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 kwnowledge and skills


Study literature

  • Zboril,F., Hanacek,P.: Artificial Intelligence, Texts, BUT Brno, 1990, ISBN 80-214-0349-7
  • Marik,V., Stepankova,O., Lazansky,J. and others: Artificial Intelligence (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

  1. Introduction, types of AI problems, solving problem methods (BFS, DFS, DLS, IDS)
  2. Solving problem methods, cont. (BS, UCS, Hill Climbing, Simulated annealing, Backtracking, Forward checking)
  3. Solving problem methods, cont. (GS, BestFS, A*, IDA, SMA, Heuristic repair)
  4. Solving problem methods, cont. (Problem decomposition, AND/OR graphs)
  5. Methods of game playing (minimax, alpha-beta, games with unpredictability)
  6. Logic and AI, resolution and it's application in problem solving
  7. Implementation of basic search algorithms in PROLOG
  8. Implementation of basic search algorithms in LISP
  9. Machine learning
  10. Fundamentals of pattern recognition theory
  11. Principles of computer vision
  12. Principles of natural language processing
  13. Principles of expert systems

Syllabus - others, projects and individual work of students

  • Individual (problem solving)

Progress assessment

  • Written mid-term exam
  • Project
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