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.