The objective of the course is to offer a complete introduction to the probability theory necessary for the field of artificial intelligence, combining breadth and depth, and offering the basic material as well as the discussion of recent developments in the field.

Review of fundamentals of probability theory. Multivariate models: Joint distribution of several variables. Multivariate normal distribution. Linear Gaussian systems. Mix models. Maximum likelihood. Regression and classification with MV. Expectation-maximization algorithm. Model selection criteria. Information theory: Entropy and relative entropy. Linear models: Logistic, linear regression and generalized linear models. Non-parametric models: Classification and clustering with KNN. Probabilistic classifiers. kernel methods. Bagging, random forest, boosting.

Teaching presentations accompanied by electronic material, such as digital presentations e-learning activities Theoretical-practical lessons, synchronous teaching tutorials Team work Individual student work Home works and