Mathematical Foundations for Data Analysis (Paperback, 1st ed. 2021)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Springer Series in the Data Sciences
Release date:	March 2022
First published:	2021
Authors:	Jeff M. Phillips
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	287
Edition:	1st ed. 2021
ISBN-13:	978-3-03-062343-2
Categories:	Books > Science & Mathematics > Mathematics > Numerical analysis Books > Science & Mathematics > Mathematics > Combinatorics & graph theory Promotions
LSN:	3-03-062343-2
Barcode:	9783030623432

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