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001			978-981-15-7877-9
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008			210803s2021 si | s |||| 0|eng d
020			_a9789811578779 _9978-981-15-7877-9
024	7		_a10.1007/978-981-15-7877-9 _2doi
050		4	_aQ334-342
050		4	_aTA347.A78
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072		7	_aUYQ _2thema
082	0	4	_a006.3 _223
100	1		_aSuzuki, Joe. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aStatistical Learning with Math and Python _h[electronic resource] : _b100 Exercises for Building Logic / _cby Joe Suzuki.
250			_a1st ed. 2021.
264		1	_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2021.
300			_aXI, 256 p. 1 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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			_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		0	_aArtificial intelligence.
650		0	_aMachine learning.
650		0	_aComputational intelligence.
650		0	_aArtificial intelligence _xData processing.
650	1	4	_aArtificial Intelligence.
650	2	4	_aMachine Learning.
650	2	4	_aStatistical Learning.
650	2	4	_aComputational Intelligence.
650	2	4	_aData Science.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9789811578762
776	0	8	_iPrinted edition: _z9789811578786
856	4	0	_uhttps://doi.org/10.1007/978-981-15-7877-9
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_cSPRINGER
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