Course Instructor: Traian Rebedea, Costin Gabriel Chiru

Syllabus:

• Introductive elements of machine learning, statistics, information theory and decision theory.
• Linear models for regressions.
• Linear models for classifications.
• Kernel methods and Gaussian processes.
• Sparse kernel methods (Support vector machines and Relevance vector machines).
• Bayesian methods and graphical methods.
• Expectation maximization.
• Principal components analysis and Independent component analysis.
• Hidden Markov models.