STATS 216V: Introduction to Statistical Learning
Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; crossvalidation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; treebased methods, random forests and boosting; supportvector machines; Some unsupervised learning: principal components and clustering (kmeans and hierarchical). Computing is done in R, through tutorial sessions and homework assignments. This mathlight course is offered remotely only via video segments (MOOC style). TAs will host remote weekly office hours using an online platform such as Google Hangout or BlueJeans. There are four homework assignments, a midterm, and final exam. Prerequisites: first courses in statistics, linear algebra, and computing.
Terms: Sum

Units: 3

Grading: Letter or Credit/No Credit
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