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Average Course Rating

Introduces statistical and computational foundations of modern statistical machine learning. Acquaints students with modern statistical machine learning models and their statistical and theoretical underpinnings. Includes topics: regression and classification, resampling methods (cross-validation and bootstrap), model and variable selection, tree-based methods for regression and classification, functional regression models, unsupervised learning, support vector machines, ensemble methods, deep learning, visualization of large datasets. Includes example applications of cancer prognosis from microarray data, graphical models for data visualization, and a prediction of survival using high-dimensional predictors.