000			02057nam a22002177a 4500
005			20240206182844.0
008			240206b |||||||| |||| 00| 0 eng d
020			_a9789811578762
082			_a519.5 _bSUZ
100			_aSuzuki, Joe
245			_aStatistical learning with math and python: _b100 exercises for building logic
260			_bSpringer _aSingapore _c2022
300			_axi, 256 p.
365			_aEURO _b35.00
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. (https://link.springer.com/book/10.1007/978-981-15-7877-9#about-this-book)
650			_aLogic, Symbolic and mathematical _915129
650			_aMathematical statistics _913214
650			_aPython (Computer program language) _913655
650			_aMachine learning _915068
942			_cBK _2ddc
999			_c5704 _d5704