LEADER 01157nam 2200337 n 450 001 996391892203316 005 20221108044554.0 035 $a(CKB)1000000000678307 035 $a(EEBO)2240915247 035 $a(UnM)99851485 035 $a(EXLCZ)991000000000678307 100 $a19920402d1633 uy | 101 0 $aeng 135 $aurbn||||a|bb| 200 12$aA match at mid-night$b[electronic resource] $eA pleasant com?die: as it hath beene acted by the Children of the Revells. Written by VV.R 210 $aLondon $cPrinted by Aug. Mathewes, for William Sheares, and are to be sold at his shop in Brittaines Bursse$d1633 215 $a[72] p 300 $aVV.R. = William Rowley. 300 $aPartly in verse. 300 $aSignatures: [A]² B-I⁴ K² . 300 $aReproduction of the original in the Henry E. Huntington Library and Art Gallery. 330 $aeebo-0113 700 $aRowley$b William$f1585?-1642?$0165559 801 0$bCu-RivES 801 1$bCu-RivES 801 2$bCStRLIN 801 2$bWaOLN 906 $aBOOK 912 $a996391892203316 996 $aA match at mid-night$92426952 997 $aUNISA LEADER 03036nam 22005055 450 001 9910886099603321 005 20240831130242.0 010 $a3-662-69426-3 024 7 $a10.1007/978-3-662-69426-8 035 $a(CKB)34605007500041 035 $a(MiAaPQ)EBC31629011 035 $a(Au-PeEL)EBL31629011 035 $a(DE-He213)978-3-662-69426-8 035 $a(EXLCZ)9934605007500041 100 $a20240831d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMathematical Introduction to Data Science /$fby Sven A. Wegner 205 $a1st ed. 2024. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2024. 215 $a1 online resource (301 pages) 311 $a3-662-69425-5 327 $aPreface -- 1 What is Data (Science)? -- 2 Affine Linear, Polynomial and Logistic Regression -- 3 k-nearest Neighbors -- 4 Clustering -- 5 Graph Clustering -- 6 Best-Fit Subspaces -- 7 Singular Value Decomposition -- 8 Curse and Blessing of High Dimensionality -- 9 Concentration of Measure -- 10 Gaussian Random Vectors in High Dimensions -- 11 Dimensionality Reduction la Johnson-Lindenstrauss -- 12 Separation and Fitting of HIgh-Dimensional Gaussians -- 13 Perceptron -- 14 Support Vector Machines -- 15 Kernel Method -- 16 Neural Networks -- 17 Gradient Descent for Convex Functions -- Appendix: Selected Results of Probability Theory -- Bibliography -- Index. 330 $aThis textbook is intended for students of mathematics who have completed the foundational courses of their undergraduate studies and now want to specialize in Data Science and Machine Learning. It introduces the reader to the most important topics in the latter areas focusing on rigorous proofs and a systematic understanding of the underlying ideas. The textbook comes with 121 classroom-tested exercises. Topics covered include k-nearest neighbors, linear and logistic regression, clustering, best-fit subspaces, principal component analysis, dimensionality reduction, collaborative filtering, perceptron, support vector machines, the kernel method, gradient descent and neural networks. The author Sven A. Wegner earned his PhD in Functional Analysis in 2010. After several international academic positions, he is currently affiliated with the University of Hamburg (Germany). 606 $aQuantitative research 606 $aArtificial intelligence$xData processing 606 $aData Analysis and Big Data 606 $aData Science 615 0$aQuantitative research. 615 0$aArtificial intelligence$xData processing. 615 14$aData Analysis and Big Data. 615 24$aData Science. 676 $a001.422 676 $a005.7 700 $aWegner$b Sven A$01771320 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910886099603321 996 $aMathematical Introduction to Data Science$94258027 997 $aUNINA