This course introduces students to the foundational theory and key algorithms involved in statistical learning. Topics include principal components analysis, k-means clustering, hierarchical clustering, linear and quadratic discriminant analysis, Bayes risk and naïve Bayes classifiers, penalized regression methods, logistic regression, k-nearest neighbors classifiers, random forests and bagging, as well as support vector machines. Issues of over-fitting, the bias-variance trade-off, and cross validation are also covered. Students will use the statistical software R to implement any algorithms taught in this course.
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