LEADER 01263nam--2200373---450- 001 990000754050203316 010 $a2-11-003653-2 035 $a0075405 035 $aUSA010075405 035 $a(ALEPH)000075405USA01 035 $a0075405 100 $a20011120d1996----km-y0itay0103----ba 101 0 $afre 102 $aFR 105 $a||||||||001yy 200 1 $alittérature et politique$edeux siècles de vie politique à travers les oeuvres littéraires$fMichel Mopin$gpréface de Robert Badinter 210 $aParis$cLa documentation française$dcopyr. 1996 215 $aXIV, 341 p.$d24 cm 225 2 $a<> études de La documentation française$iSociété 410 0$12001$a<> études de La documentation française$iSociété 606 $aLetteratura e politica$yFrancia$zSec. 18.-20. 676 $a840.9005 700 1$aMOPIN,$bMichel$0549977 702 1$aBADINTER,$bRobert 801 0$aIT$bsalbc$gISBD 912 $a990000754050203316 951 $aII f B 705$b160438 L.M.$cII f B$d00078473 959 $aBK 969 $auma 979 $aCHIARA$b40$c20011120$lUSA01$h1243 979 $c20020403$lUSA01$h1723 979 $aPATRY$b90$c20040406$lUSA01$h1652 996 $aLittérature et politique$9964909 997 $aUNISA LEADER 03956nam 22006975 450 001 9910765481403321 005 20250828123143.0 010 $a3-031-41337-7 024 7 $a10.1007/978-3-031-41337-7 035 $a(CKB)28853344900041 035 $a(MiAaPQ)EBC30943286 035 $a(Au-PeEL)EBL30943286 035 $a(DE-He213)978-3-031-41337-7 035 $a(EXLCZ)9928853344900041 100 $a20231114d2023 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aFundamentals of Supervised Machine Learning $eWith Applications in Python, R, and Stata /$fby Giovanni Cerulli 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (416 pages) 225 1 $aStatistics and Computing,$x2197-1706 311 08$a9783031413360 327 $aPreface -- The Ontology of Machine Learning -- The Statistics of Machine Learning -- Model Selection and Regularization -- Discriminant Analysis, Nearest Neighbor and Support Vector Machines -- Tree Modelling -- Artificial Neural Networks -- Deep Learning -- Sentiment Analysis -- Index. . 330 $aThis book presents the fundamental theoretical notions of supervised machine learning along with a wide range of applications using Python, R, and Stata. It provides a balance between theory and applications and fosters an understanding and awareness of the availability of machine learning methods over different software platforms. After introducing the machine learning basics, the focus turns to a broad spectrum of topics: model selection and regularization, discriminant analysis, nearest neighbors, support vector machines, tree modeling, artificial neural networks, deep learning, and sentiment analysis. Each chapter is self-contained and comprises an initial theoretical part, where the basics of the methodologies are explained, followed by an applicative part, where the methods are applied to real-world datasets. Numerous examples are included and, for ease of reproducibility, the Python, R, and Stata codes used in the text, along with the related datasets, are available online. The intended audience is PhD students, researchers and practitioners from various disciplines, including economics and other social sciences, medicine and epidemiology, who have a good understanding of basic statistics and a working knowledge of statistical software, and who want to apply machine learning methods in their work. 410 0$aStatistics and Computing,$x2197-1706 606 $aMachine learning 606 $aStatistics$xComputer programs 606 $aStatistics 606 $aBiometry 606 $aSocial sciences$xStatistical methods 606 $aStatistical Learning 606 $aMachine Learning 606 $aStatistical Software 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aBiostatistics 606 $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy 615 0$aMachine learning. 615 0$aStatistics$xComputer programs. 615 0$aStatistics. 615 0$aBiometry. 615 0$aSocial sciences$xStatistical methods. 615 14$aStatistical Learning. 615 24$aMachine Learning. 615 24$aStatistical Software. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aBiostatistics. 615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 676 $a519.50285 676 $a006.31 700 $aCerulli$b Giovanni$0790241 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910765481403321 996 $aFundamentals of Supervised Machine Learning$93644887 997 $aUNINA