Course details
Fundamentals of Artificial Intelligence (in English)
IZUe Acad. year 2024/2025 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
Course coordinator
Language of instruction
Completion
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
Lecturer
Instructor
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.
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.
Prerequisite knowledge and skills
None.
Study literature
- Zbořil,F., Hanáček,P.: Umělá inteligence, Skripta VUT v Brně, VUT v Brně, 1990, ISBN 80-214-0349-7
- Mařík,V., Štěpánková,O., Lažanský,J. a kol.: Umělá inteligence (1)+(2), ACADEMIA Praha, 1993 (1), 1997 (2), ISBN 80-200-0502-1
- Luger,G.F., Stubblefield,W.A.: Artificial Intelligence, The Benjamin/Cummings Publishing Company, Inc., 1993, ISBN 0-8053-4785-2
Syllabus of lectures
- Introduction, types of AI problems, solving problem methods (BFS, DFS, DLS, IDS).
- Solving problem methods, cont. (BS, UCS, Backtracking, Forward checking).
- Solving problem methods, cont. (BestFS, GS, A*, IDA, SMA, Hill Climbing, Simulated annealing, Heuristic repair).
- Solving problem methods, cont. (Problem decomposition, AND/OR graphs).
- Methods of game playing (minimax, alpha-beta, games with unpredictability).
- Logic and AI, resolution and it's application in problem solving.
- Knowledge representation (representational schemes).
- Implementation of basic search algorithms in PROLOG.
- Implementation of basic search algorithms in LISP.
- Machine learning.
- Fundamentals of pattern recognition theory.
- Principles of computer vision.
- Principles of natural language processing.
Syllabus of computer exercises
- Problem solving - simple programs.
- Problem solving - games playing.
- PROLOG language - basic information.
- PROLOG language - simple individual programs.
- LISP language - basic information.
- LISP language - simple individual programs.
- Simple programs for pattern recognition.
Progress assessment
- Mid-term written examination - 20 points
- Programs in computer exercises - 20 points
Written mid-term exam
Schedule