Mathematical Foundations of Big Data Analytics
Mathematical Foundations of Big Data Analytics
Résumé
In this textbook, basic mathematical models used in Big Data Analytics are presented and application-oriented references to relevant practical issues are made.
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Mathematical Foundations of Big Data Analytics by Vladimir Shikhman
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.“This book is apt for courses that introduce the fundamentals of data science/big data analytics at the graduate level… The book can be used in courses devoted to the foundational mathematics of data science and analytics. It should be noted that sound mathematical knowledge … is required for reading. The case studies and exercises make it a quality teaching material.” (Bálint Molnár, Computing Reviews, August 19, 2022)
“Mathematical foundations of big data analytics is a very welcome and timely addition to the growing area of big data analytics. … Mathematical foundations are very carefully covered in each chapter, which justifies the title. There is a good listing of references for further study, as well as an index for easy reference. This book could be the basis for a one-semester graduate level course with an emphasis on mathematical foundations, supplemented by good programming projects.” (S. Lakshmivarahan, Computing Reviews, July 5, 2021)
| SKU | Non disponible |
| ISBN 13 | 9783662625200 |
| ISBN 10 | 3662625202 |
| Titre | Mathematical Foundations of Big Data Analytics |
| Auteur | Vladimir Shikhman |
| État | Non disponible |
| Type de reliure | Paperback |
| Éditeur | Springer Gabler |
| Année de publication | 2021-02-12 |
| Nombre de pages | 273 |
| Note de couverture | La photo du livre est présentée à titre d'illustration uniquement. La reliure, la couverture ou l'édition réelle peuvent varier. |
| Note | Non disponible |