1. Agents, types of agents, agent properties. Browse - informed strategies.

2. Search - informed strategies. Games.

3. Logical agents, propositional and predicate database knowledge.

4. Inference of the predicate in the knowledge base.

5. Planning.

6. likelihood naive Bayesian classifier, Bayesian network.

7. Bayesian network, exact and approximate inference in Bayesian network.

8. Using Bayesian networks in artificial intelligence. Introduction to the use of probability theory in games.

9. Monte Carlo method in games.

10. The classic theory of time series, time series models.

11. Use of Bayesian networks inference in time series with uncertainty.

12. Markov priocesy, Kalman filter, the use of artificial intelligence.

13. Decision Theory: simple and complex decision-making, decision trees.

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