Title:

Artificial Intelligence

Code:UIN
Ac.Year:ukončen 2005/2006
Term:Summer
Curriculums:
ProgrammeBranchYearDuty
EI-BC-3VTB2nd Stage/2nd YearElective
EI-MSC-3VTN2ndCompulsory
EI-MSC-5VTI2nd Stage/2nd YearCompulsory
Language:Czech, English
Credits:6
Completion:examination (written)
Type of
instruction:
Hour/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Hours:39001214
 ExaminationTestsExercisesLaboratoriesOther
Points:60200020
Guarantee:Zbořil František V., doc. Ing., CSc., DITS
Lecturer:Zbořil František V., doc. Ing., CSc., DITS
Instructor:Jurka Pavel, Ing., DITS
Mazal Zdeněk, Ing., DITS
Zbořil František, doc. Ing., Ph.D., DITS
Faculty:Faculty of Information Technology BUT
Department:Department of Intelligent Systems FIT BUT
Follow-ups:
Neural Networks (NEU), DITS
 
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 recognitions. Students acquire base information about computer vision, natural language processing and expert systems.
Description:
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. Basic principles of natural language processing. Basic principles of expert systems.
Knowledge and skills required for the course:
None.
Learning outcomes and competences:
Students acquire knowledge of various approaches of problem solving and basic information about machine learning, computer vision, natural language processing and expert systems. They will be able to create programs using heuristics for problem solving.
Syllabus of lectures:
  1. Introduction, types of AI problems, solving problem methods (BFS, DFS, DLS, IDS)
  2. Solving problem methods, cont. (BS, UCS, Hill Climbing, Simulated annealing, Backtracking, Forward checking)
  3. Solving problem methods, cont. (GS, BestFS, A*, IDA, SMA, 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. Implementation of basic search algorithms in PROLOG
  8. Implementation of basic search algorithms in LISP
  9. Machine learning
  10. Fundamentals of pattern recognition theory
  11. Principles of computer vision
  12. Principles of natural language processing
  13. Principles of expert systems
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.
Syllabus - others, projects and individual work of students:
  1. Individual (problem solving)
Fundamental literature:
  1. Russel,S., Norvig.,P.: Artificial Intelligence, Prentice-Hall, Inc., 1995, ISBN 0-13-360124-2, second edition 2003, ISBN 0-13-080302-2 
  2. Luger,G.F., Stubblefield,W.A.: Artificial Intelligence, The Benjamin/Cummings Publishing Company, Inc., 1993, ISBN 0-8053-4785-2
Study literature:
  1. Zboril,F., Hanacek,P.: Artificial Intelligence, Texts, BUT Brno, 1990, ISBN 80-214-0349-7
  2. Marik,V., Stepankova,O., Lazansky,J. and others: Artificial Intelligence (1)+(2), ACADEMIA Praha, 1993 (1), 1997 (2), ISBN 80-200-0502-1
Progress assessment:
  • Written mid-term exam
  • Project