Course details

Fundamentals of Artificial Intelligence

IZUe Acad. year 2016/2017 Winter semester 4 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. Base principles of natural language processing. Application fields of artificial intelligence.

Guarantor

Zbořil František, doc. Ing., Ph.D. (DITS)

Language of instruction

English

Completion

Credit+Examination (written)

Time span

  • 26 hrs lectures
  • 13 hrs pc labs

Assessment points

  • 60 pts final exam (written part)
  • 20 pts mid-term test (written part)
  • 20 pts numeric exercises

Department

Department of Intelligent Systems (UITS)

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 knowledge 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.

Syllabus of computer exercises

  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.

Progress assessment

At least 15 points earned during semester.

Controlled instruction

Written mid-term exam

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