Mathematical Foundations of Big Data Analytics by Vladimir Shikhman

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)

Vladimir Shikhman is a professor of Economathematics at Chemnitz University of Technology.David Müller is one of his doctoral students.

SKU	Unavailable
ISBN 13	9783662625200
ISBN 10	3662625202
Title	Mathematical Foundations of Big Data Analytics
Author	Vladimir Shikhman
Condition	Unavailable
Binding Type	Paperback
Publisher	Springer Gabler
Year published	2021-02-12
Number of pages	273
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
Note	Unavailable