Mathematical Pictures at a Data Science Exhibition (Hardcover)

Simon Foucart

ISBN 10: 1316518884 / ISBN 13: 9781316518885

Published by Cambridge University Press, Cambridge, 2022

About this Item

Hardcover. This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts. This text explores a diverse set of data science topics through a mathematical lens, helping mathematicians become acquainted with data science in general, and machine learning, optimal recovery, compressive sensing, optimization, and neural networks in particular. It will also be valuable to data scientists seeking mathematical sophistication. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9781316518885

Bibliographic Details

• Title: Mathematical Pictures at a Data Science Exhibition (Hardcover)
• Publisher: Cambridge University Press, Cambridge
• Publication Date: 2022
• Language: English
• ISBN 10: 1316518884
• ISBN 13: 9781316518885
• Binding: Hardcover
• Condition: new

About this title

Synopsis

This text provides deep and comprehensive coverage of the mathematical background for data science, including machine learning, optimal recovery, compressed sensing, optimization, and neural networks. In the past few decades, heuristic methods adopted by big tech companies have complemented existing scientific disciplines to form the new field of Data Science. This text embarks the readers on an engaging itinerary through the theory supporting the field. Altogether, twenty-seven lecture-length chapters with exercises provide all the details necessary for a solid understanding of key topics in data science. While the book covers standard material on machine learning and optimization, it also includes distinctive presentations of topics such as reproducing kernel Hilbert spaces, spectral clustering, optimal recovery, compressed sensing, group testing, and applications of semidefinite programming. Students and data scientists with less mathematical background will appreciate the appendices that provide more background on some of the more abstract concepts.

About the Author

Simon Foucart is Professor of Mathematics at Texas A&M University, where he was named Presidential Impact Fellow in 2019. He has previously written, together with Holger Rauhut, the influential book A Mathematical Introduction to Compressive Sensing (2013).