000			03969cam a2200481Ii 4500
003			OCoLC
005			20251211143651.0
008			210801s2021 nyua ob 001 0 eng d
020			_a9781071614181
020			_a9781071614204
020			_z9781071614174
041			_aEnglish
049			_aMAIN
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223 _bJAM
100	1		_aJames, Gareth
100	1		_q(Gareth Michael), _eauthor.
245	1	3	_aAn introduction to statistical learning : _bwith applications in R / _cGareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani.
250			_aSecond edition.
260			_aBoston : _bSpringer, _c2022.
300			_a607p. : _billustrations (black and white, and colour) ; _c24 cm.
490	1		_aSpringer texts in statistics.
500			_aPrevious edition: New York: Springer, 2013.
504			_aIncludes bibliographical references and index.
505	0		_aPreface -- 1 Introduction -- 2 Statistical Learning -- 3 Linear Regression -- 4 Classification -- 5 Resampling Methods -- 6 Linear Model Selection and Regularization -- 7 Moving Beyond Linearity -- 8 Tree-Based Methods -- 9 Support Vector Machines -- 10 Deep Learning -- 11 Survival Analysis and Censored Data -- 12 Unsupervised Learning -- 13 Multiple Testing -- Index.
506			_aAccess restricted to subscribing institutions.
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 naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
650		0	_aMathematical statistics.
650		0	_aMathematical models.
650		0	_aR (Computer program language)
700	1		_aWitten, Daniela,
700	1		_aHastie, Trevor,
700	1		_aTibshirani, Robert,
700	1		_eauthor.
700	1		_eauthor.
700	1		_eauthor.
776	0	8	_iPrint version: _aJames, Gareth (Gareth Michael). _tIntroduction to statistical learning. _bSecond edition. _dBoston : Springer, 2021 _z9781071614174 _w(OCoLC)1242740707.
830		0	_aSpringer texts in statistics.
856	4	0	_uhttps://ezproxy.lib.gla.ac.uk/login?url=https://link.springer.com/10.1007/978-1-0716-1418-1
856	4	0	_zConnect to resource
907			_a.b37980452
942			_2ddc _cBOOKS
999			_c46349 _d46349