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 Title The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
 Author(s) Trevor Hastie, Robert Tibshirani, Jerome Friedman
 Publisher: Springer; 2nd edition (2016); eBook (Online Corrected 12th printing  Jan 13, 2017)
 Hardcover 745 pages
 eBook PDF (764 pages)
 Language: English
 ISBN10: 0387848576
 ISBN13: 9780387848570
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Book Description
During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology.
This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting  the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, nonnegative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
About the Authors
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie codeveloped much of the statistical modeling software and environment in R/SPLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is coauthor of the very successful An Introduction to the Bootstrap. Friedman is the coinventor of many datamining tools including CART, MARS, projection pursuit and gradient boosting.
 Machine Learning
 Statistics and SAS Programming
 Data Analysis and Data Mining
 Probability, Stochastic Process, Queueing Theory, etc.
 The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition
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