Faculty of Information Technology, BUT

Course details

Fundamentals of Artificial Intelligence (in English)

IZUe Acad. year 2018/2019 Winter semester 4 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. Base principles of natural language processing. Application fields of artificial intelligence.


Language of instruction



Credit+Examination (written)

Time span

26 hrs lectures, 13 hrs pc labs

Assessment points

60 exam, 20 half-term test, 20 exercises




Abdulrahman Wassem, Ing. (DITS FIT BUT)


* This course is prepared for incoming Erasmus+ students only, and it is instructed in English.
* This course will be open if a certain/sure minimum of enrolled students is at least five students.

Subject specific learning outcomes and competences

Students acquire knowledge of various approaches of problem solving and base information about machine learning, computer vision and natural language processing. 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 recognition. Students acquire base information about computer vision and natural language processing.

Why is the course taught

In the IZU course, students should gain knowledge what artificial intelligence is, realize that the artificial intelligence does not mean artificial being, but that it is serious and very useful branch of computer science. Furthermore, students will learn basic techniques and approaches to solving problems that they can use then for creation of artificial intelligent systems.

Prerequisite kwnowledge and skills


Study literature

  • Zboril,F., Hanacek,P.: Artificial intelligence, Texts, BUT Brno, 1990, ISBN 80-214-0349-7
  • Marik,V., Stepanková,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, Backtracking, Forward checking).
  3. Solving problem methods, cont. (BestFS, GS, A*, IDA, SMA, Hill Climbing, Simulated annealing, 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. Knowledge representation (representational schemes).
  8. Implementation of basic search algorithms in PROLOG.
  9. Implementation of basic search algorithms in LISP.
  10. Machine learning.
  11. Fundamentals of pattern recognition theory.
  12. Principles of computer vision.
  13. Principles of natural language processing.

Progress assessment

  • Mid-term written examination - 20 points
  • Programs in computer exercises - 20 points

Controlled instruction

Written mid-term exam

Exam prerequisites

At least 2 points earned during semester.


Monlecture2018-12-03 A113 09:0010:50 INTE IZUe, mimořádný termín pro předn
Monexam2019-01-14 A113 13:0014:50 INTE
Tuelecture2018-12-18 A113 11:0012:50 INTE
Tuelecturelectures A112 13:0014:50 INTE
Tueexam2019-01-08 A112 14:0015:50 INTE
Tuecomp.lab7., 8., 9., 10., 11., 12., 13. of lectures N203 15:0016:50 INTE počítačové cvičení
Tuecomp.lab7., 8., 9., 10., 11., 12., 13. of lectures M103 17:0018:50 INTE počítačové cvičení
Thulecture2018-12-13 A112 16:0017:50
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