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020 _a9789811575686
_9978-981-15-7568-6
024 7 _a10.1007/978-981-15-7568-6
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
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 R
_h[electronic resource] :
_b100 Exercises for Building Logic /
_cby Joe Suzuki.
250 _a1st ed. 2020.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2020.
300 _aXI, 217 p. 68 illus., 65 illus. in color.
_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 R 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 1 4 _aArtificial Intelligence.
650 2 4 _aMachine Learning.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811575679
776 0 8 _iPrinted edition:
_z9789811575693
856 4 0 _uhttps://doi.org/10.1007/978-981-15-7568-6
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c176881
_d176881