The Mathematics of Data
by Michael W. Mahoney (Author, Editor), John C. Duchi (Author, Editor), Anna C. Gilbert (Author, Editor)

About this book
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.

Brief contents
Preface
Introduction
Lectures on Randomized Numerical Linear Algebra
Optimization Algorithms for Data Analysis
Introductory Lectures on Stochastic Optimization
Randomized Methods for Matrix Computations
Four Lectures on Probabilistic Methods for Data Science
Homological Algebra and Data

Series: IAS/PARK CITY Mathematics (Book 25)
Pages: 325 pages
Publisher: American Mathematical Society (November 15, 2018)
Language: English
ISBN-10: 1470435756
ISBN-13: 978-1470435752