Drexel University 3 credits BST
This course is a survey of statistical learning methods and will cover major techniques and concepts for both supervised and unsupervised learning. Topics include penalized regression and classification, support vector machines, kernel methods, model selection, clustering, boosting, CART and random forests, and ensemble learning. Students will learn how and when to apply statistical learning techniques, their comparative strengths and weaknesses, how to critically evaluate the performance of learning algorithms, and principles for appropriate application to health science problems. The statistical programming language R will be used throughout.
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