Course description
This course provide a wide discussion of ML topics such as ML (applications, problems, Probability Theory, basic algorithms), density estimation (estimation, sampling, Exponential Families), Online Learning and Boosting, Kernel machines and Function Spaces, Linear Models, Support Vector Classification, ML methods(Parametric Methods, Multivariate Methods), clustering, Decision Trees, graphical models.