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Mathematical foundations for data an...

Phillips, Jeff M.

Mathematical foundations for data analysis

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mathematical foundations for data analysisby Jeff M. Phillips.
Author:	Phillips, Jeff M.
Published:	Cham :Springer International Publishing :2021.
Description:	xvii, 287 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Data miningMathematics.
Online resource:	https://doi.org/10.1007/978-3-030-62341-8
ISBN:	9783030623418$q(electronic bk.)

Mathematical foundations for data analysis
Phillips, Jeff M.

Mathematical foundations for data analysis[electronic resource] /by Jeff M. Phillips. - Cham :Springer International Publishing :2021. - xvii, 287 p. :ill. (some col.), digital ;24 cm. - Springer series in the data sciences,2365-5674. - Springer series in the data sciences..

Probability review -- Convergence and sampling -- Linear algebra review -- Distances and nearest neighbors -- Linear Regression -- Gradient descent -- Dimensionality reduction -- Clustering -- Classification -- Graph structured data -- Big data and sketching.

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.

ISBN: 9783030623418$q(electronic bk.)

Standard No.: 10.1007/978-3-030-62341-8doiSubjects--Topical Terms:

735581
Data mining
--Mathematics.

LC Class. No.: QA76.9.D343 / P499 2021

Dewey Class. No.: 006.3120151

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520 $a 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.
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