Course detail

Fundamentals of Artificial Intelligence (in English)

FIT-IZUeAcad. year: 2025/2026

Problem solving, searching state space, decomposition into subtasks, playing games. Logic and resolution methods, basics of the PROLOG language. Principles of machine learning. Feature-based and structural image recognition. Basics of computer vision. Basic principles of working with natural language. Application areas of artificial intelligence.

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 a serious and very useful branch of computer science. Furthermore, students will learn basic techniques and approaches to solving problems that they can use them for the creation of artificially intelligent systems.

Equiment (freely available)

  • SWI PROLOG - verze 6.2.6, Copyright (c) 1990-2012 University of Amsterdam, VU Amsterdam

Exam prerequisites
To be allowed to take the final exam, the student has to obtain at least

  • 20 points from computer labs and
  • 20 poits from the mid-semester exam

Language of instruction

English

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

doc. Ing. František Zbořil, Ph.D.

Department

Department of Intelligent Systems (UITS)

Offered to foreign students

Of all faculties

Entry knowledge

None.

Rules for evaluation and completion of the course

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

Written mid-term exam

Aims

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.
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.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Not applicable.

Recommended reading

Luger,G.F., Stubblefield,W.A.: Artificial Intelligence, The Benjamin/Cummings Publishing Company, Inc., 1993, ISBN 0-8053-4785-2
Mařík,V., Štěpánková,O., Lažanský,J. a kol.: Umělá inteligence (1)+(2), ACADEMIA Praha, 1993 (1), 1997 (2), ISBN 80-200-0502-1

Zbořil,F., Hanáček,P.: Umělá inteligence, Skripta VUT v Brně, VUT v Brně, 1990, ISBN 80-214-0349-7

Elearning

eLearning: currently opened course

Classification of course in study plans

  • Programme IT-BC-1H Bachelor's

    specialization BCH , 0 year of study, winter semester, recommended course

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

doc. Ing. František Zbořil, Ph.D.
Ing. Jaroslav Rozman, Ph.D.

Syllabus

  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.

Exercise in computer lab

13 hod., compulsory

Teacher / Lecturer

Ing. Jaroslav Rozman, Ph.D.

Syllabus

  1. Problem solving - uninformed search
  2. Problem solving - informed search
  3. Game play - minmax, alpha-beta pruning
  4. Test
  5. Resolution method
  6. Prolog - simple predicates
  7. Simple programs for image recognition

Elearning

eLearning: currently opened course