LEADER 04277nam 22007695 450 001 9910983081503321 005 20251003125016.0 010 $a9783031302763 010 $a3031302761 024 7 $a10.1007/978-3-031-30276-3 035 $a(MiAaPQ)EBC31922673 035 $a(Au-PeEL)EBL31922673 035 $a(CKB)37723032500041 035 $a(DE-He213)978-3-031-30276-3 035 $a(EXLCZ)9937723032500041 100 $a20250225d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 12$aA First Course in Statistical Learning $eWith Data Examples and Python Code /$fby Johannes Lederer 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (298 pages) 225 1 $aStatistics and Computing,$x2197-1706 311 08$a9783031302756 311 08$a3031302753 327 $aPart I: Data -- Chapter 1: Fundamentals of Data -- Chapter 2: Exploratory Data Analysis -- Chapter 3: Unsupervised Learning -- Part II: Inferential Data Analyses -- Chapter 4: Linear Regression -- Chapter 5: Logistic Regression -- Chapter 6: Regularization -- Part III: Machine Learning -- Chapter 7: Support-Vector Machines -- Chapter 8: Deep Learning. 330 $aThis textbook introduces the fundamental concepts and methods of statistical learning. It uses Python and provides a unique approach by blending theory, data examples, software code, and exercises from beginning to end for a profound yet practical introduction to statistical learning. The book consists of three parts: The first one presents data in the framework of probability theory, exploratory data analysis, and unsupervised learning. The second part on inferential data analysis covers linear and logistic regression and regularization. The last part studies machine learning with a focus on support-vector machines and deep learning. Each chapter is based on a dataset, which can be downloaded from the book's homepage. In addition, the book has the following features: A careful selection of topics ensures rapid progress. An opening question at the beginning of each chapter leads the reader through the topic. Expositions are rigorous yet based on elementary mathematics. More than two hundred exercises help digest the material. A crisp discussion section at the end of each chapter summarizes the key concepts and highlights practical implications. Numerous suggestions for further reading guide the reader in finding additional information. This book is for everyone who wants to understand and apply concepts and methods of statistical learning. Typical readers are graduate and advanced undergraduate students in data-intensive fields such as computer science, biology, psychology, business, and engineering, and graduates preparing for their job interviews. 410 0$aStatistics and Computing,$x2197-1706 606 $aMachine learning 606 $aStatistics$xComputer programs 606 $aStatistics 606 $aArtificial intelligence$xData processing 606 $aStatistical Learning 606 $aMachine Learning 606 $aStatistical Software 606 $aStatistical Theory and Methods 606 $aApplied Statistics 606 $aData Science 606 $aEstadística$2thub 606 $aAprenentatge automàtic$2thub 606 $aMineria de dades$2thub 608 $aLlibres electrònics$2thub 615 0$aMachine learning. 615 0$aStatistics$xComputer programs. 615 0$aStatistics. 615 0$aArtificial intelligence$xData processing. 615 14$aStatistical Learning. 615 24$aMachine Learning. 615 24$aStatistical Software. 615 24$aStatistical Theory and Methods. 615 24$aApplied Statistics. 615 24$aData Science. 615 7$aEstadística 615 7$aAprenentatge automàtic 615 7$aMineria de dades 676 $a006.31 700 $aLederer$b Johannes$01076955 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910983081503321 996 $aA First Course in Statistical Learning$94317286 997 $aUNINA