000			02614nam a22002657a 4500
999			_c1454 _d1454
005			20211208151150.0
008			211208b ||||| |||| 00| 0 eng d
020			_a9781071614174
082			_a519.5 _bJAM
100			_aJames, Gareth _94435
245			_aAn introduction to statistical learning: with applications in R
250			_a2nd
260			_bSpringer _aNew York _c2021
300			_axv, 607 p.
365			_aEURO _b84.99
504			_aTable of content Front Matter Pages i-xv PDF Introduction Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 1-14 Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 15-57 Linear Regression Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 59-128 Classification Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 129-195 Resampling Methods Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 197-223 Linear Model Selection and Regularization Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 225-288 Moving Beyond Linearity Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 289-326 Tree-Based Methods Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 327-365 Support Vector Machines Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 367-402 Deep Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 403-460 Survival Analysis and Censored Data Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 461-495 Unsupervised Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 497-552 Multiple Testing Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani Pages 553-595
520			_aPresents an essential statistical learning toolkit for practitioners in science, industry, and other fields Demonstrates application of the statistical learning methods in R Includes new chapters on deep learning, survival analysis, and multiple testing Covers a range of topics, such as linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and deep learning Features extensive color graphics for a dynamic learning experience
650			_aMathematical statistics _9837
650			_aR (Computer program language) _91512
650			_aMathematical models _9851
700			_aWitten, Daniela _94436
700			_aHastie, Trevor _94437
700			_aTibshirani, Robert _94438
942			_2ddc _cBK