LEADER 02802nam 2200469 450 001 996464405303316 005 20220421130439.0 010 $a981-16-1446-6 024 7 $a10.1007/978-981-16-1446-0 035 $a(CKB)4100000011995246 035 $a(DE-He213)978-981-16-1446-0 035 $a(MiAaPQ)EBC6690581 035 $a(Au-PeEL)EBL6690581 035 $a(OCoLC)1263870828 035 $a(PPN)257355626 035 $a(EXLCZ)994100000011995246 100 $a20220421d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSparse estimation with math and R $e100 exercises for building logic /$fJoe Suzuki 205 $a1st ed. 2021. 210 1$aSingapore :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (X, 234 p. 54 illus., 46 illus. in color.) 311 $a981-16-1445-8 320 $aIncludes bibliographical references. 327 $aChapter 1: Linear Regression -- Chapter 2: Generalized Linear Regression -- Chapter 3: Group Lasso -- Chapter 4: Fused Lasso -- Chapter 5: Graphical Model -- Chapter 6: Matrix Decomposition -- Chapter 7: Multivariate Analysis. 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 sparse estimation by considering math problems and building R programs. Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers? insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that 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 15 lectures (90 mins each). Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis. 606 $aMultivariate analysis 615 0$aMultivariate analysis. 676 $a519.535 700 $aSuzuki$b Joe$0846228 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464405303316 996 $aSparse Estimation with Math and R$91896754 997 $aUNISA