Hardcover ISBN:  9781470435752 
Product Code:  PCMS/25 
List Price:  $125.00 
MAA Member Price:  $112.50 
AMS Member Price:  $100.00 
eBook ISBN:  9781470449902 
Product Code:  PCMS/25.E 
List Price:  $112.00 
MAA Member Price:  $100.80 
AMS Member Price:  $89.60 
Hardcover ISBN:  9781470435752 
eBook: ISBN:  9781470449902 
Product Code:  PCMS/25.B 
List Price:  $237.00 $181.00 
MAA Member Price:  $213.30 $162.90 
AMS Member Price:  $189.60 $144.80 
Hardcover ISBN:  9781470435752 
Product Code:  PCMS/25 
List Price:  $125.00 
MAA Member Price:  $112.50 
AMS Member Price:  $100.00 
eBook ISBN:  9781470449902 
Product Code:  PCMS/25.E 
List Price:  $112.00 
MAA Member Price:  $100.80 
AMS Member Price:  $89.60 
Hardcover ISBN:  9781470435752 
eBook ISBN:  9781470449902 
Product Code:  PCMS/25.B 
List Price:  $237.00 $181.00 
MAA Member Price:  $213.30 $162.90 
AMS Member Price:  $189.60 $144.80 

Book DetailsIAS/Park City Mathematics SeriesVolume: 25; 2018; 325 ppMSC: Primary 15; 52; 60; 62; 65; 68; 90
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 volume is a copublication of the AMS, IAS/Park City Mathematics Institute, and Society for Industrial and Applied Mathematics
Titles in this series are copublished with the Institute for Advanced Study/Park City Mathematics Institute.
ReadershipGraduate students and researchers interested in applied mathematics of data.

Table of Contents

Articles

Petros Drineas and Michael Mahoney — Lectures on randomized numerical linear algebra

Stephen Wright — Optimization algorithms for data analysis

John Duchi — Introductory lectures on stochastic optimization

PerGunnar Martinsson — Randomized methods for matrix computations

Roman Vershynin — Four lectures on probabilistic methods for data science

Robert Ghrist — Homological algebra and data


Additional Material

Reviews

What should you expect from a book titled 'The Mathematics of Data'? Nearly anything. There are numerous elementary books with similar titles that don't go far beyond showing the reader how to compute the standard deviation. But what if you saw that the book was published by AMS and SIAM? That changes everything. You know it won't be elementary, and it will probably be high quality, which is indeed the case here.
John D. Cook, MAA Reviews


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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 volume is a copublication of the AMS, IAS/Park City Mathematics Institute, and Society for Industrial and Applied Mathematics
Titles in this series are copublished with the Institute for Advanced Study/Park City Mathematics Institute.
Graduate students and researchers interested in applied mathematics of data.

Articles

Petros Drineas and Michael Mahoney — Lectures on randomized numerical linear algebra

Stephen Wright — Optimization algorithms for data analysis

John Duchi — Introductory lectures on stochastic optimization

PerGunnar Martinsson — Randomized methods for matrix computations

Roman Vershynin — Four lectures on probabilistic methods for data science

Robert Ghrist — Homological algebra and data

What should you expect from a book titled 'The Mathematics of Data'? Nearly anything. There are numerous elementary books with similar titles that don't go far beyond showing the reader how to compute the standard deviation. But what if you saw that the book was published by AMS and SIAM? That changes everything. You know it won't be elementary, and it will probably be high quality, which is indeed the case here.
John D. Cook, MAA Reviews