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portada Mathematical Foundations for Data Analysis
Type
Physical Book
Author
Publisher
Language
English
Pages
287
Format
Paperback
Dimensions
23.4 x 15.6 x 1.7 cm
Weight
0.44 kg.
ISBN13
9783030623432
Edition No.
1
Categories

Mathematical Foundations for Data Analysis

Jeff M. Phillips (Author) · Springer · Paperback

Mathematical Foundations for Data Analysis - Phillips, Jeff M.

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Synopsis "Mathematical Foundations for Data Analysis"

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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