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Free Online Course: Statistical Learning


With a free MOOC from Stanford, dive into statistical learning with the respected professors who literally wrote the book on it.

Statistical Learning Respected Stanford professors Trevor Hastie and Robert Tibshirani, along with Martin Wainwright, not long ago released a new book titled "Statistical Learning with Sparsity: The Lasso and Generalizations," which is available for purchase via its website, and has recently been made freely available as a PDF download.

Starting this week, the week of January 11, 2016, Hastie and Tibshirani are running a free MOOC via Stanford Online titled, quite simply, "Statistical Learning". Directly from the course website:

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 course is based on the material covered in the book "An Introduction to Statistical Learning, with Applications in R," which is also freely available as a PDF on the book's website.

For a commitment of 3-5 hours per week, you can dive into statistical learning with the professors who literally wrote the book on it.

Trevor Hastie is the John A. Overdeck Professor of Statistics at Stanford University. Prior to joining Stanford University, Professor Hastie worked at AT&T Bell Laboratories, where he helped develop the statistical modeling environment popular in the R computing system. Professor Hastie is known for his research in applied statistics, particularly in the fields of data mining, bioinformatics, and machine learning. He has published five books and over 180 research articles in these areas. In 2014, he received the Emanuel and Carol Parzen Prize for Statistical Innovation. He earned a PhD from Stanford University.

Robert Tibshirani is a professor in the Departments of Statistics and Health Research and Policy at Stanford University. He has authored five books, co-authored three books, and published over 200 research articles. He has made important contributions to the analysis of complex datasets, including the lasso and significance analysis of microarrays (SAM). He also co-authored the first study that linked cell phone usage with car accidents, a widely cited article that has played a role in the introduction of legislation that restricts the use of phones while driving. Professor Tibshirani was a recipient of the prestigious COPSS Presidents’ Award in 1996 and was elected to the National Academy of Sciences in 2012.