Mathematical Foundations of Big Data Analy...

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Beschreibung

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.

Über den Autor

Vladimir Shikhman is a professor of Economathematics at Chemnitz University of Technology.

David Müller is one of his doctoral students.

Zusammenfassung

Covers all relevant techniques commonly used in Big Data Analytics?

Standardized structure and size of the chapters: motivation, results, case-study, exercises

Recommended and developed for university courses in Germany, Austria and Switzerland

Provides complete solutions for the exercises

Inhaltsverzeichnis

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.

Details

Erscheinungsjahr:	2021
Genre:	Informatik , Mathematik , Medizin , Naturwissenschaften , Technik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Inhalt:	xi 273 S. 32 s/w Illustr. 21 farbige Illustr. 273 p. 53 illus. 21 illus. in color. Textbook for German language market.
ISBN-13:	9783662625200
ISBN-10:	3662625202
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Müller, David Shikhman, Vladimir
Auflage:	1st edition 2021
Hersteller:	Springer Berlin Springer Berlin Heidelberg
Verantwortliche Person für die EU:	Springer Gabler in Springer Science + Business Media, Tiergartenstr. 15-17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße:	240 x 168 x 16 mm
Von/Mit:	David Müller (u. a.)
Erscheinungsdatum:	12.02.2021
Gewicht:	0,488 kg

Artikel-ID: 118987665