Mathematical Foundations for Data Analysis (Springer Series in the Data Sciences)

Jeff M. Phillips

ISBN 10: 3030623432 / ISBN 13: 9783030623432

Published by Springer; 1st ed. 2021 edition (31 Mar. 2022), 2022

About this Item

Seller Inventory # LU-9783030623432

Bibliographic Details

• Title: Mathematical Foundations for Data Analysis (Springer Series in the Data Sciences)
• Publisher: Springer; 1st ed. 2021 edition (31 Mar. 2022)
• Publication Date: 2022
• Language: English
• ISBN 10: 3030623432
• ISBN 13: 9783030623432
• Binding: Paperback
• Edition: 1st ed. 2021.
• Condition: New
• Dimensions: 15.49W x 1.85H x 23.5D centimeters 15.49 centimeters width by 1.85 centimeters height by 23.5 centimeters depth

About this title

Synopsis

This textbook, suitable for an early undergraduate up to a graduate course, provides an overview of many basic principles and techniques needed for modern data analysis. In particular, this book was designed and written as preparation for students planning to take rigorous Machine Learning and Data Mining courses. It introduces key conceptual tools necessary for data analysis, including concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis. It also surveys basic techniques in supervised (regression and classification) and unsupervised learning (dimensionality reduction and clustering) through an accessible, simplified presentation. Students are recommended to have some background in calculus, probability, and linear algebra.  Some familiarity with programming and algorithms is useful to understand advanced topics on computational techniques.

About the Author

Jeff M. Phillips is an Associate Professor in the School of Computing within the University of Utah.  He directs the Utah Center for Data Science as well as the Data Science curriculum within the School of Computing.  His research is on algorithms for big data analytics, a domain with spans machine learning, computational geometry, data mining, algorithms, and databases, and his work regularly appears in top venues in each of these fields.  He focuses on a geometric interpretation of problems, striving for simple, geometric, and intuitive techniques with provable guarantees and solve important challenges in data science.  His research is supported by numerous NSF awards including an NSF Career Award.