Faculty of Information Technology, BUT

Course details

Fundamentals of Artificial Intelligence

IZUe Acad. year 2016/2017 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.

Guarantor

Language of instruction

English

Completion

Credit+Examination (written)

Time span

26 hrs lectures, 13 hrs pc labs

Assessment points

60 exam, 20 half-term test, 20 exercises

Department

Lecturer

Instructor

Abdulrahman Wassem, Ing. (DITS FIT BUT)

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.

Prerequisite kwnowledge and skills

None.

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 15 points earned during semester.
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