Mathematical Foundations of Big Data Analytics

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

• 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)