===== Course Material =====

===Course books===

//Main source//

[[http://www-stat.stanford.edu/~tibs/ElemStatLearn//|The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition]] (Springer Series in Statistics) by Trevor Hastie, Robert Tibshirani, and Jerome Friedman.

//Additional sources//

[[http://link.springer.com/book/10.1007%2F978-1-4614-7138-7|An Introduction to Statistical Learning with Applications in R]] (Springer Texts in Statistics) by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.

[[http://link.springer.com/book/10.1007%2F978-3-319-44048-4|Statistical Learning from a Regression Perspective, Second Edition]] (Springer Texts in Statistics) by Richard A. Berk.


  * The complete books can be downloaded (pdf) from the above links.
 

===Associated webpages/resources===


[[http://www-stat.stanford.edu/~tibs/ElemStatLearn//|Webpage for "The Elements of..."]] 

[[http://waxworksmath.com/Authors/G_M/Hastie/WriteUp/weatherwax_epstein_hastie_solutions_manual.pdf|"A Solution Manual and Notes for: The Elements of..."]]

[[http://www-bcf.usc.edu/~gareth/ISL/index.html|Webpage for "An Introduction to..."]] 

[[http://blog.princehonest.com/stat-learning|Unofficial Solutions for "An Introduction to..."]]



===Recommended reading===



(Course book in an earlier course on Multivariate analysis:) \\
Johnson, R.A. and Wichern, D.W.: "Applied Multivariate Statistical Analysis".

Hastie, T.J. and Tibshirani, R.J.: "Generalized Additive Models"



===Computer programs===

[[http://cran.r-project.org/web/packages/ElemStatLearn/index.html|ElemStatLearn]] R-package which inlcudes data sets, functions and examples from the course book.