Title:

Fundamentals of Artificial Intelligence

Code:IZU
Ac.Year:2011/2012
Term:Summer
Study plans:
ProgramBranchYearDuty
IT-BC-3BIT2ndCompulsory
Language:Czech
Private info:http://www.fit.vutbr.cz/study/courses/IZU/private/
Credits:4
Completion:accreditation+exam (written)
Type of
instruction:
Hour/semLecturesSem. ExercisesLab. exercisesComp. exercisesOther
Hours:2600130
 ExaminationTestsExercisesLaboratoriesOther
Points:60202000
Guarantee:Zbořil František V., doc. Ing., CSc., DITS
Lecturer:Zbořil František, Ing., Ph.D., DITS
Zbořil František V., doc. Ing., CSc., DITS
Instructor:Král Jiří, Ing., DITS
Luža Radim, Ing., DITS
Malačka Ondřej, Ing., DITS
Rozman Jaroslav, Ing., Ph.D., DITS
Samek Jan, Ing., Ph.D., DITS
Faculty:Faculty of Information Technology BUT
Department:Department of Intelligent Systems FIT BUT
Substitute for:
Artificial Intelligence (UIN), 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 recognition. Students acquire base information about computer vision and natural language processing.
Description:
Problem solving: State space search (BFS, DFS, DLS, IDS, BS, UCS, Backtracking, Forward checking, Min-conflict, BestFS, GS, A*, Hill Climbing, Simulated annealing methods), problem decomposition (And Or graphs), games playing (Mini-Max and Alfa-Beta algorithms). Knowledge representation - basic schemes. AI languages (PROLOG, LISP) and implementations of basic search algorithms in these languages. Machine learning principles. Statistical and structural pattern recognition. Fundamentals of computer vision. Base principles of natural language processing. Application fields of artificial intelligence.
Knowledge and skills required for the course:
None.
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.
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, 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.
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.
Fundamental literature:
  1. 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
Study 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
Controlled instruction:
Mid-term written test
Progress assessment:
  • Mid-term written text - 20 points
  • Programs in computer exercises - 20 points
Exam prerequisites:
At least 15 points earned during semester.