LEADER 03148nam 2200505 450 001 996464427403316 005 20220419114908.0 010 $a981-15-7877-X 024 7 $a10.1007/978-981-15-7877-9 035 $a(CKB)4100000011994899 035 $a(DE-He213)978-981-15-7877-9 035 $a(MiAaPQ)EBC6689292 035 $a(Au-PeEL)EBL6689292 035 $a(OCoLC)1263026447 035 $a(PPN)257354824 035 $a(EXLCZ)994100000011994899 100 $a20220419d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical learning with math and python $e100 exercises for building logic /$fJoe Suzuki 205 $a1st ed. 2021. 210 1$aSingapore :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (XI, 256 p. 446 illus., 170 illus. in color.) 311 $a981-15-7876-1 327 $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 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. 606 $aMathematical statistics 606 $aLogic, Symbolic and mathematical 606 $aPython (Computer program language) 615 0$aMathematical statistics. 615 0$aLogic, Symbolic and mathematical. 615 0$aPython (Computer program language) 676 $a519.5 700 $aSuzuki$b Joe$0846228 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464427403316 996 $aStatistical Learning with Math and Python$91890233 997 $aUNISA