
The Mathematics of Data
American Mathematical Society (Publisher)
Published on 30. November 2018
Book
Hardback
325 pages
978-1-4704-3575-2 (ISBN)
Description
Data science is a highly interdisciplinary field, incorporating ideas from applied mathematics, statistics, probability, and computer science, as well as many other areas. This book gives an introduction to the mathematical methods that form the foundations of machine learning and data science, presented by leading experts in computer science, statistics, and applied mathematics. Although the chapters can be read independently, they are designed to be read together as they lay out algorithmic, statistical, and numerical approaches in diverse but complementary ways.
This book can be used both as a text for advanced undergraduate and beginning graduate courses, and as a survey for researchers interested in understanding how applied mathematics broadly defined is being used in data science. It will appeal to anyone interested in the interdisciplinary foundations of machine learning and data science.
This book can be used both as a text for advanced undergraduate and beginning graduate courses, and as a survey for researchers interested in understanding how applied mathematics broadly defined is being used in data science. It will appeal to anyone interested in the interdisciplinary foundations of machine learning and data science.
More details
Series
Language
English
Place of publication
Providence
United States
Target group
College/higher education
Dimensions
Height: 254 mm
Width: 178 mm
Weight
755 gr
ISBN-13
978-1-4704-3575-2 (9781470435752)
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Schweitzer Classification
Persons
Michael W. Mahoney, University of California, Berkeley, CA.
John C. Duchi, Stanford University, CA.
Anna C. Gilbert, University of Michigan, Ann Arbor, MI.
John C. Duchi, Stanford University, CA.
Anna C. Gilbert, University of Michigan, Ann Arbor, MI.
Content
P. Drineas and M. W. Mahoney, Lectures on randomized numerical linear algebra
S. J. Wright, Optimization algorithms for data analysis
J. C. Duchi, Introductory lectures on stochastic optimization
P.-G. Martinsson, Randomized methods for matrix computations
R. Vershynin, Four lectures on probabilistic methods for data science
R. Ghrist, Homological algebra and data.
S. J. Wright, Optimization algorithms for data analysis
J. C. Duchi, Introductory lectures on stochastic optimization
P.-G. Martinsson, Randomized methods for matrix computations
R. Vershynin, Four lectures on probabilistic methods for data science
R. Ghrist, Homological algebra and data.