Course Description
This course is a survey of statistical learning methods and will cover major techniques and concepts for both supervised and unsupervised learning. Topics covered include penalized regression and classification, support vector machines, kernel methods, model selection, matrix factorizations, graphical models, clustering, boosting, random forests, and ensemble learning. Students will learn how and when to apply statistical learning techniques, their comparative strengths and weaknesses, and how to critically evaluate the performance of learning algorithms. Students completing this course should be able to
(i) apply basic statistical learning methods to build predictive models or perform exploratory analysis,
(ii) properly tune and select statistical learning models,
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