An introduction to statistical learning : with applications in R /

"An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty year...

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Bibliographic Details
Main Authors:	James, Gareth (Gareth Michael) (Author), Witten, Daniela (Author), Hastie, Trevor (Author), Tibshirani, Robert (Author)
Format:	Book
Language:	English
Published:	New York : Springer, [2014]
Edition:	Corrected edition.
Series:	Springer texts in statistics.
Subjects:	Mathematical statistics > Problems, exercises, etc. Mathematical models > Problems, exercises, etc. R (Computer program language) Statistics. Statistique mathématique. Modèles mathématiques. Statistique mathématique > Problèmes et exercices. Modèles mathématiques > Problèmes et exercices. Mathematical models. Mathematical statistics. Statistik. Maschinelles Lernen. Problems, exercises, etc.

Table of Contents:

Introduction
Statistical learning
Linear regression
Classification
Resampling methods
Linear model selection and regularization
Moving beyond linearity
Tree-based methods
Support vector machines
Unsupervised learning.