Mathematical Foundations for Data Analysis Hardback
by Jeff M. Phillips
Part of the Springer Series in the Data Sciences series
Hardback
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Description
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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In Stock - low on stock, only 1 copy remainingFree UK DeliveryEstimated delivery 2-3 working days
- Format:Hardback
- Pages:287 pages, 108 Illustrations, color; 1 Illustrations, black and white; XVII, 287 p. 109 illus., 108
- Publisher:Springer Nature Switzerland AG
- Publication Date:30/03/2021
- Category:
- ISBN:9783030623401
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- EPUB from £42.49
- Paperback / softback from £37.28
Information
-
In Stock - low on stock, only 1 copy remainingFree UK DeliveryEstimated delivery 2-3 working days
- Format:Hardback
- Pages:287 pages, 108 Illustrations, color; 1 Illustrations, black and white; XVII, 287 p. 109 illus., 108
- Publisher:Springer Nature Switzerland AG
- Publication Date:30/03/2021
- Category:
- ISBN:9783030623401