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008			210803s2021 si | s |||| 0|eng d
020			_a9789811578779
024	7		_a10.1007/978-981-15-7877-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA276 _b2021 EB
100	1		_aSuzuki, Joe _eautor _9681524
245	1	0	_aStatistical Learning with Math and Python : _b100 Exercises for Building Logic _cby Joe Suzuki
250			_aFirst edition 2021
264		1	_aSingapore _bSpringer International Publising _c2021
300			_a1 recurso en línea (XI, 256 páginas) _b446 ilustraciones, 170 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
505	0		_aChapter 1: Linear Algebra -- Chapter 2: Linear Regression -- Chapter 3: Classification -- Chapter 4: Resampling -- Chapter 5: Information Criteria -- Chapter 6: Regularization -- Chapter 7: Nonlinear Regression -- Chapter 8: Decision Trees -- Chapter 9: Support Vector Machine -- Chapter 10: Unsupervised Learning.
520	3		_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.
988			_aSpringer_Computer_2021
650		7	_2embne _9138936 _aEstadística matemática
650		7	_2embne _9139136 _aLógica matemática
650		7	_2embne _9161036 _aPython (Lenguaje de programación)
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-15-7877-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eu _zSI