Definition of probabilistic model and basic properties of probability, conditional probability, Bayes theorems, random variables, random vectors and their characteristics, limit theorems, introduction to Markov chain theory, probabilistic theory of information, regression model with normally distributed errors, introduction to theory of parameter estimation and statistical hypothesis testing.
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