000			03159nam a22003137a 4500
003			ZW-GwMSU
005			20241008124132.0
008			241008b |||||||| |||| 00| 0 eng d
020			_a9781071614204
040			_bEnglish _cMSULIB _erda
050	0	0	_aQA76.9.M35 INT
100	1		_aJames, Gareth _eauthor
245	1	3	_aAn introduction to statistical learning : _bwith applications in R / _ccreated by Gareth James , Daniela Witten, Trevor Hastie and Robert Tibshirani
250			_aSecond edition
264		1	_bSpringer, _c2021
300			_axv, 607 pages : _billustrations (some colored) ; _c24 cm.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
504			_aIncludes index
505			_aIntroduction Statistical learning Linear regression Classification Resampling methods Linear model selection and regularization Moving beyond linearity Tree-based methods Support vector machines Deep learning Survival analysis and censored data Unsupervised learning Multiple testing
520			_aAn 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."
650		0	_aMathematical models
650		0	_aStatistics
700	1		_aWitten, Daniela _eauthor
700	1		_aHastie, Trevor _eauthor
700	1		_aTibshirani, Robert _eauthor
942			_2lcc _cB
999			_c167585 _d167585