000			a
999			_c33770 _d33770
008			250306b xxu||||| |||| 00| 0 eng d
020			_a9789811578762
082			_a519.5 _bSUZ
100			_aSuzuki, Joe
245			_aStatistical learning with math and Python : 100 exercises for building logic
260			_bSpringer, _c2021 _aSingapore :
300			_axi, 256 p. ; _bill. (some col.), _c24 cm
365			_b35 _c€ _d93.20
504			_aIncludes index.
520			_aThe most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of machine learning and data science by considering math problems and building Python programs. As the preliminary part, Chapter 1 provides a concise introduction to linear algebra, which will help novices read further to the following main chapters. Those succeeding chapters present essential topics in statistical learning: linear regression, classification, resampling, information criteria, regularization, nonlinear regression, decision trees, support vector machines, and unsupervised learning. Each chapter mathematically formulates and solves machine learning problems and builds the programs. The body of a chapter is accompanied by proofs and programs in an appendix, with exercises at the end of the chapter. Because the book is carefully organized to provide the solutions to the exercises in each chapter, readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 12 lectures. Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning.
650			_aMathematical statistics
650			_aSymbolic and mathematical
650			_aComputer program language
650			_aDecision tree
650			_aData frame
650			_aEigenvalues
650			_aInner product;
650			_aLinear regression
650			_aOrthogonal matrix
650			_aPrediction intervals
942			_2ddc _cBK