LEADER 03354nam 2200553 450 001 9910427703303321 005 20221108170710.0 010 $a981-15-7568-1 024 7 $a10.1007/978-981-15-7568-6 035 $a(CKB)4100000011515630 035 $a(DE-He213)978-981-15-7568-6 035 $a(MiAaPQ)EBC6380791 035 $a(MiAaPQ)EBC6525473 035 $a(Au-PeEL)EBL6525473 035 $a(OCoLC)1202750831 035 $a(PPN)260306223 035 $a(EXLCZ)994100000011515630 100 $a20211014d2020 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical learning with math and R $e100 exercises for building logic /$fJoe Suzuki 205 $a1st ed. 2020. 210 1$aGateway East, Singapore :$cSpringer,$d[2020] 210 4$dİ2020 215 $a1 online resource (XI, 217 p. 70 illus., 65 illus. in color.) 300 $aIncludes index. 311 $a981-15-7567-3 327 $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. 330 $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. 606 $aMachine learning$xMathematics$vTextbooks 606 $aLogic, Symbolic and mathematical$vTextbooks 606 $aArtificial intelligence$xMathematics$vTextbooks 606 $aR (Computer program language) 615 0$aMachine learning$xMathematics 615 0$aLogic, Symbolic and mathematical 615 0$aArtificial intelligence$xMathematics 615 0$aR (Computer program language). 676 $a006.31 700 $aSuzuki$b Joe$0846228 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910427703303321 996 $aStatistical learning with math and R$91898875 997 $aUNINA