Mathematical Foundations for Data Analysis

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Sprache: Englisch

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Beschreibung

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.

Über den Autor

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.

Zusammenfassung

Provides accessible, simplified introduction to core mathematical language and concepts

Integrates examples of key concepts through geometric illustrations and Python coding

Addresses topics in locality sensitive hashing, graph-structured data, and big data processing as well as basic linear algebra

Includes perspectives on ethics in data

Inhaltsverzeichnis

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.

Erscheinungsjahr:	2021
Fachbereich:	Wahrscheinlichkeitstheorie
Genre:	Mathematik
Rubrik:	Naturwissenschaften & Technik
Medium:	Buch
Reihe:	Springer Series in the Data Sciences
Inhalt:	xvii 287 S. 1 s/w Illustr. 108 farbige Illustr. 287 p. 109 illus. 108 illus. in color.
ISBN-13:	9783030623401
ISBN-10:	3030623408
Sprache:	Englisch
Herstellernummer:	978-3-030-62340-1
Ausstattung / Beilage:	HC runder Rücken kaschiert
Einband:	Gebunden
Autor:	Phillips, Jeff M.
Auflage:	1st ed. 2021
Hersteller:	Springer International Publishing Springer Series in the Data Sciences
Maße:	241 x 160 x 22 mm
Von/Mit:	Jeff M. Phillips
Erscheinungsdatum:	30.03.2021
Gewicht:	0,685 kg

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