Fundamentals of Artificial Intelligence

• 26 hrs lectures
• 13 hrs projects

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

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.

• 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 logic and with their applications.
• Students will learn how to use basic methods of machine learning.
• Students will acquaint with fundamentals of expert systems, machine vision and natural language processing.
• Students will acquaint with fundamentals of multiagent systems.

   - Basic knowledge of programming in any procedural programming language.    - Knowledge of secondary school level mathematics.

• Russell,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
• Ertel, W.: Introduction to Artificial Intelligence, Springer, second edition 2017, ISSN 1863-7310
• Pool, D. L., Mackworth, A. K.: Artificial Intelligence, Cambridge University Press, 2010,  ISBN-13 978-0-521-51900-7

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.
4. Solving optimization problems using algorithms inspired by nature.
5. Methods of game playing (two players).
6. Logic and AI, resolution and it's application in problem-solving and planning.
7. PROLOG language and its use in AI.
8. Machine learning.  
9. Pattern recognition.
10. Principles of expert systems.
11. Principles of computer vision.
12. Principles of natural language processing.
13. Introduction to agent systems. 

1. Project dealing with state space search and game playing
2. Project dealing with logic and PROLOG language
3. Two projects dealing with machine learning and classifiers

• Mid-term written examination - 20 points.
• Projects (homeworks) - 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.

Missed lessons (exercises and tests) can be substituted only exceptionally, after proving that the absences had legitimate reasons.