Free Stanford online course of statistical learning

willchang

This is an introductory-level 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; 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 methods are discussed: principal components and clustering (k-means and hierarchical).

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.

Classes Start: Jan 19, 2015
Classes End: Apr 03, 2015
Course Staff: Prof. Trevor Hastie, Prof. Rob Tibshirani
Price: Free
class.stanford.edu/courses/HumanitiesandScience/StatLearning/Winter2015/about

dolremi

顶一下，两位大牛主讲啊

CleverBeaver

很有用，讲课也很生动