Course details
Fundamentals of Artificial Intelligence
IZU Acad. year 2011/2012 Summer semester 4 credits
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.
Guarantor
Language of instruction
Completion
Time span
- 26 hrs lectures
- 13 hrs pc labs
Department
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
- 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
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, 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
Syllabus of lectures
- Introduction, types of AI problems, solving problem methods (BFS, DFS, DLS, IDS).
- Solving problem methods, cont. (BS, UCS, Backtracking, Forward checking, Min-conflict).
- Solving problem methods, cont. (BestFS, GS, A*, IDA, SMA, Hill Climbing, Simulated annealing).
- 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
At least 15 points earned during semester.
Controlled instruction
Mid-term written test