This textbook provides a solid mathematical basis for understanding popular data science algorithms for clustering and classification and shows that an in-depth understanding of the mathematics powering these algorithms gives insight into the underlying data. It presents a step-by-step derivation of these algorithms, outlining their implementation from scratch in a computationally sound way.

Mathematics of Data Science: A Computational Approach to Clustering and Classification

• proposes different ways of visualizing high-dimensional data to unveil hidden internal structures, and
• includes graphical explanations and computed examples using publicly available data sets in nearly every chapter to highlight similarities and differences among the algorithms.

About the Author
Daniela Calvetti is the James Wood Williamson Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University. Her research interests, strongly rooted in numerical analysis, include inverse problems, uncertainty quantification, and mathematical modeling, with a specific focus on human metabolism and the brain. She is a member of SIAM and the International Society for Cerebral Blood Flow and Metabolism.

Erkki Somersalo is a professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University, with a background in analysis and partial differential equations. His research interests include computational inverse problems, with an emphasis on Bayesian methods, and applications to a wide range of areas, particularly to medical imaging. He is a founding member of the Finnish Inverse Problems Society and a member of the Finnish Academy of Sciences and Letters and SIAM. He is also a fellow of the Institute of Physics, UK.


Book Information
ISBN 9781611976366
Author Daniela Calvetti
Format Paperback
Page Count 189
Imprint Society for Industrial & Applied Mathematics,U.S.
Publisher Society for Industrial & Applied Mathematics,U.S.
Weight(grams) 422g