Fundamentals of Artificial Intelligence

• 26 hrs lectures
• 13 hrs pc labs

• 60 pts final exam (written part)
• 20 pts mid-term test (written part)
• 20 pts numeric exercises

• Students will learn terminology in Artificial Intelligence field both in Czech and in English language.
• Students will learn read and so partly write logic and functional programs.

• Students will acquaint with problem solving methods based on state space search and on decomposition problem into sub-problems.
• Students will acquaint with basic game playing methods of two players.
• Students will learn to solve optimization problems.
• Students will acquaint with fundamentals of propositional and predicate logics and with their applications.
• Students will learn how to use basic methods of machine learning.
• Students will acquaint with fundamentals of machine vision and natural language processing.

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 expert systems, computer vision and natural language processing.

• Basic knowledge of the programming in any procedural programming language.
• Knowledge of secondary school level matematics.

• 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

• 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 

1. Introduction, Artificial Intelligence (AI) definition, types of AI problems, solving problem methods.
2. State space search methods.
3. Solving methods using decomposition problems into sub-problems (AND/OR graphs).
4. Solving of optimization problems using algorithms inspired by nature - short introduction into Genetic algorithms, ACO (Ant Colony Optimization) and PSO (Particle Swarm Optimization).
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. Classical classifiers, perceptron.
11. Expert systems.
12. Principles of computer vision.
13. Principles of natural language processing.

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.

At least 15 points earned during semester (mid-term test + programs in computer exercises).

• Mid-term written examination - 20 points.
• Programs in computer exercises - 20 points.
• Final written examination - 60 points; The minimal number of points which can be obtained from the final written examination is 25. Otherwise, no points will be assigned to a student.