We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R, Second Edition'. 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.ISLR2: Introduction to Statistical Learning, Second Edition. Color graphics and real-world examples are used to illustrate the methods presented.
An Introduction To Statistical Learning Uic Free Online Version
Springer has agreed to this, so no need to worry about copyright. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.A free online version of An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013) is available from that website. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience.
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Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Her research focuses largely on statistical machine learning techniques for the analysis of complex, messy, and large-scale data, with an emphasis on unsupervised learning.Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning.
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