Using machine learning algorithms effectively requires understanding their theoretical and conceptual basis. Covers mathematical and probabilistic foundations of machine learning, placing learning methods in a unified framework based on Bayesian reasoning. Students will acquire skills for formulating models, deriving optimization algorithms, and choosing effective approaches for a given learning problem. Topics include parameter estimation, optimization, linear classification and regression, regularization, and probabilistic graphical models.
Using machine learning algorithms effectively requires understanding their theoretical and conceptual basis. Covers mathematical and probabilistic foundations of machine learning, placing learning methods in a unified framework based on Bayesian reasoning. Students will acquire skills for formulating models, deriving optimization algorithms, and choosing effective approaches for a given learning problem. Topics include parameter estimation, optimization, linear classification and regression, regularization, and probabilistic graphical models.
