Course detail

Fundamentals of Artificial Intelligence

FIT-IZUAcad. year: 2010/2011

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

Czech

Number of ECTS credits

4

Mode of study

Not applicable.

Guarantor

doc. Ing. František Zbořil, CSc.

Department

Department of Intelligent Systems (UITS)

Learning outcomes of the course unit

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.

Prerequisites

None.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Not applicable.

Assesment methods and criteria linked to learning outcomes

At least 15 points earned during semester.

Course curriculum

  1. Introduction, types of AI problems, solving problem methods (BFS, DFS, DLS, IDS).
  2. Solving problem methods, cont. (BS, UCS, Backtracking, Forward checking, Min-conflict).
  3. Solving problem methods, cont. (BestFS, GS, A*, IDA, SMA, Hill Climbing, Simulated annealing).
  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.

Work placements

Not applicable.

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.

Specification of controlled education, way of implementation and compensation for absences

Written mid-term exam

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

  • Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., 1995, ISBN 0-13-360124-2, second edition 2003, ISBN 0-13-080302-2, third edition 2010, ISBN 0-13-604259-7
  • Luger,G.F.: Artificial Intelligence - Structures and strategies for Complex Problem Solving, 6th Edition,
    Pearson Education, Inc., 2009, ISBN-13: 978-0-321-54589-3, ISBN-10: 0-321-54589-3 

Recommended reading

  • Russel,S., Norvig,P.: Artificial Intelligence, Prentice-Hall, Inc., 1995, ISBN 0-13-360124-2, second edition 2003, ISBN 0-13-080302-2, third edition 2010, ISBN 0-13-604259-7

Classification of course in study plans

  • Programme IT-BC-3 Bachelor's

    branch BIT , 2. year of study, summer semester, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

doc. Ing. František Zbořil, CSc.

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, optionally

Teacher / Lecturer

doc. Ing. František Zbořil, CSc.

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.

E-learning texts

František V. Zbořil: Základy umělé inteligence (cs)