Statistical Learning

Graduate

Modern statistical methods for supervised and unsupervised learning with large and complex data. Topics include linear regression, classification, resampling methods, model selection and regularization, smooth regression, tree-based models, support vector machines, principal components and dimension reduction, clustering and statistical graphics.