STATS 216: 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 via video segments (MOOC style), and inclass problem solving sessions. Prereqs: Introductory courses in statistics or probability (e.g.,
Stats 60 or
Stats 101), linear algebra (e.g.,
Math 51), and computer programming (e.g.,
CS 105).
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Tibshirani, R. (PI)
;
Donnat, C. (TA)
;
Feldman, M. (TA)
...
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Instructors:
Tibshirani, R. (PI)
;
Donnat, C. (TA)
;
Feldman, M. (TA)
;
Tay, J. (TA)
;
Walsh, D. (TA)
;
Zhao, Q. (TA)
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 a final exam, all of which are administered remotely. Prereqs: Introductory courses in statistics or probability (e.g.,
Stats 60 or
Stats 101), linear algebra (e.g.,
Math 51), and computer programming (e.g.,
CS 105).
Terms: Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Hastie, T. (PI)
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