Statistical Learning and Data Science [Flipped]

3 credits · Graduate

Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; Some unsupervised learning: principal components and clustering (k-means and hierarchical). Prerequisites: STATS 117, CS 106A, MATH 51. Recommended: STATS 191 or STATS 203. See https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.This class is taught in flipped format, where students watch videos at home and work on assignments during class.