1. Artificial intelligence, its contents, methodology.

2. Problem solving.

3. State space, search for solution.

4. Uninformed search.

5. Heuristic search.

6. Games problem solving.

7. Machine learning.

8. Neural networks and evolutionary algorithms.

9. Mathematical logic for artificial intelligence.

10. Knowledge representation.

11. Uncertain knowledge and its representation.

12. Planning.

13. Multiagent systems.

Type of methodology: Combination of lecture and hands-on

Participants receive the certificate of attendance: Yes

Paid training activity for participants: Yes, for all

Participants prerequisite knowledge: Numerical methods (linear algebra, statistics) Domain-specific background knowledge