Course detail

Fundamentals of Artificial Intelligence (in English)

FIT-IZUeAcad. year: 2023/2024

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

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

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
Luger,G.F., Stubblefield,W.A.: Artificial Intelligence, The Benjamin/Cummings Publishing Company, Inc., 1993, ISBN 0-8053-4785-2

eLearning

eLearning: currently opened course

Classification of course in study plans

  • Programme IT-BC-1H Bachelor's

    specialization BCH , any year of study, winter semester, recommended

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

doc. Ing. František Zbořil, 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 hours, compulsory

Teacher / Lecturer

Ing. Jaroslav Rozman, Ph.D.

Syllabus

  1. Problem solving - simple programs.
  2. Problem solving - games playing.
  3. PROLOG language - basic information.
  4. PROLOG language - simple individual programs.
  5. LISP language - basic information.
  6. LISP language - simple individual programs.
  7. Simple programs for pattern recognition.

eLearning

eLearning: currently opened course