Data Science and Machine Learning 2

This course introduces students to the basic theories, concepts, and techniques of machine learning and gives them a glimpse of the state-of-the-art methods in this area. Topics covered include Bayesian estimation and decision theory, maximum likelihood estimation, nonparametric techniques, linear discriminant analysis, computational learning theory, support vector machines and kernel methods, boosting, clustering, dimensional reduction, and deep learning.