LEADER 03308oam 2200445 450 001 9910484942203321 005 20210701141406.0 010 $a3-662-62521-0 024 7 $a10.1007/978-3-662-62521-7 035 $a(CKB)4100000011763236 035 $a(DE-He213)978-3-662-62521-7 035 $a(MiAaPQ)EBC6478115 035 $a(PPN)253856167 035 $a(EXLCZ)994100000011763236 100 $a20210701d2021 uy 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMathematical foundations of big data analytics /$fVladimir Shikhman, David Müller 205 $a1st ed. 2021. 210 1$aBerlin, Germany :$cSpringer Gabler,$d[2021] 210 4$d©2021 215 $a1 online resource (XI, 273 p. 53 illus., 21 illus. in color. Textbook for German language market.) 311 $a3-662-62520-2 327 $aPreface -- 1 Ranking -- 2 Online Learning -- 3 Recommendation Systems -- 4 Classification -- 5 Clustering -- 6 Linear Regression -- 7 Sparse Recovery -- 8 Neural Networks -- 9 Decision Trees -- 10 Solutions. 330 $aIn this textbook, basic mathematical models used in Big Data Analytics are presented and application-oriented references to relevant practical issues are made. Necessary mathematical tools are examined and applied to current problems of data analysis, such as brand loyalty, portfolio selection, credit investigation, quality control, product clustering, asset pricing etc. ? mainly in an economic context. In addition, we discuss interdisciplinary applications to biology, linguistics, sociology, electrical engineering, computer science and artificial intelligence. For the models, we make use of a wide range of mathematics ? from basic disciplines of numerical linear algebra, statistics and optimization to more specialized game, graph and even complexity theories. By doing so, we cover all relevant techniques commonly used in Big Data Analytics. Each chapter starts with a concrete practical problem whose primary aim is to motivate the study of a particular Big Data Analytics technique. Next, mathematical results follow ? including important definitions, auxiliary statements and conclusions arising. Case-studies help to deepen the acquired knowledge by applying it in an interdisciplinary context. Exercises serve to improve understanding of the underlying theory. Complete solutions for exercises can be consulted by the interested reader at the end of the textbook; for some which have to be solved numerically, we provide descriptions of algorithms in Python code as supplementary material. This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland. The authors Vladimir Shikhman is a professor of Economathematics at Chemnitz University of Technology. David Müller is one of his doctoral students. 606 $aBig data$xMathematics 615 0$aBig data$xMathematics. 676 $a005.7 700 $aShikhman$b Vladimir$01082399 702 $aMuller$b David 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910484942203321 996 $aMathematical foundations of big data analytics$92597699 997 $aUNINA LEADER 00883nam a2200241 i 4500 001 991002680369707536 008 070723s2007 it 001 0 ita d 020 $a9788883358470 035 $ab13563841-39ule_inst 040 $aDip.to Filosofia$bita 082 0 $a400 100 1 $aAzzariti Fumaroli, Luigi$0517655 245 12$aL'oblio del linguaggio /$cLuigi Azzariti-Fumaroli 260 $aMilano :$bGuerini e Associati,$c2007 300 $a380 p. ;$c21 cm 440 0$aIstituto Italiano per gli studi filosofici$pSocrates ;$v29 650 4$aFilosofia$xLinguaggio 907 $a.b13563841$b23-07-07$c23-07-07 912 $a991002680369707536 945 $aLE005 149 AZZ01. 01$g1$i2005000184123$lle005$o-$pE41.50$q-$rl$s- $t0$u0$v0$w0$x0$y.i14520850$z23-07-07 996 $aOblio del linguaggio$91021889 997 $aUNISALENTO 998 $ale005$b23-07-07$cm$da $e-$fita$git $h2$i0