EAN: 9781071614174

AN INTRODUCTION TO STATISTICAL LEARNING - GARET...

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 years. This book presents some of the most important modeling and prediction
techniques  along with relevant applications. Topics include linear regression  classification
resampling methods  shrinkage approaches  tree-based methods  support vector machines 
clustering  deep learning  survival analysis  multiple testing  and more. Color graphics and
real-world examples are used to illustrate the methods presented. Since the goal of this
textbook is to facilitate the use of these statistical learning techniques by practitioners in
science  industry  and other fields  each chapter contains a tutorial on implementing the
analyses and methods presented in R  an extremely popular open source statistical software
platform. Two of the authors co-wrote  The Elements of Statistical Learning  (Hastie 
Tibshirani and Friedman  2nd edition 2009)  a popular reference book for statistics and machine
learning researchers.  An Introduction to Statistical Learning  covers many of the same topics
but at a level accessible to a much broader audience. This book is targeted at statisticians
and non-statisticians alike who wish to use cutting-edge statistical learning techniques to
analyze their data. The text assumes only a previous course in linear regression and no
knowledge of matrix algebra. This Second Edition features new chapters on deep learning 
survival analysis  and multiple testing  as well as expanded treatments of naïve Bayes 
generalized linear models  Bayesian additive regression trees  and matrix completion. R code
has been updated throughout to ensure compatibility.

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