This is an introductorylevel course in supervised learning, with a focus on regression and classification methods. The 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 methods are discussed: principal components and clustering (kmeans and hierarchical).
This is not a mathheavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data analysis. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter.
The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013). As of January 5, 2014, the pdf for this book will be available for free, with the consent of the publisher, on the book website.
Prerequisites
First courses in statistics, linear algebra, and computing.
Instructors
Trevor Hastie, John A Overdeck Professor of Statistics, Stanford University
Robert Tibshirani, Professor in the Departments Health Research and Policy and Statistics, Stanford University
Delivery Option: 
Online

Online Course  $0.00 
Notes
Textbooks & Resources
A free online version of An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013) is available from that website. Springer has agreed to this, so no need to worry about copyright. Of course you may not distribiute printed versions of this pdf file.
You get R for free from http://cran.us.rproject.org/. Typically it installs with a click. You get RStudio from http://www.rstudio.com/ , also for free, and a similarly easy install.
Time Commitment
It will take approximately 35 hours per week to go through the materials and exercises in each section.
This course may not currently be available to learners in some states and territories.