Mathematical foundations of big data analytics

Bibliographische Detailangaben

Titel
Mathematical foundations of big data analytics
verantwortlich
Shikhman, Vladimir (VerfasserIn); Müller, David (VerfasserIn); Springer-Verlag GmbH (Verlag)
veröffentlicht
Berlin, [Heidelberg]: Springer Gabler, [2021]
© 2021
Erscheinungsjahr
2021
Erscheint auch als
Shikhman, Vladimir, 1981 - , Mathematical Foundations of Big Data Analytics, Berlin : Springer Gabler, 2021, 1 Online-Ressource (XI, 273 Seiten)
Andere Ausgaben
Mathematical Foundations of Big Data Analytics
Medientyp
Buch
Datenquelle
K10plus Verbundkatalog
Tags
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Zusammenfassung
Preface -- 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.
In 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.
Umfang
xi, 273 Seiten; Illustrationen; 24 cm x 17 cm
Sprache
Englisch
Schlagworte
RVK-Notation
  • Informatik
    • Monografien
      • Künstliche Intelligenz
        • Allgemeines
  • Mathematik
    • Monografien
      • Wahrscheinlichkeitstheorie
        • Spezielle statistische Verfahren
  • Wirtschaftswissenschaften
    • Mathematik. Statistik. Ökonometrie. Unternehmensforschung
      • Statistik
        • Theoretische Statistik
          • Regression und Korrelation, Faktoren-, Komponenten-, Diskriminanzanalyse sowie sonstiger Methoden der mehrdimensionalen Analyse. Assoziation, Kontingenz, MOS, Kausalanalyse/Pfadanalyse/LISREL
BK-Notation
54.72 Künstliche Intelligenz
31.73 Mathematische Statistik
DDC-Notation
005.7 ; 004
ISBN
9783662625200
3662625202