Jeff M. Phillips
Mathematical Foundations for Data Analysis
Mathematical Foundations for Data Analysis
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This textbook covers fundamental principles and techniques for modern data analysis, preparing students for Machine Learning and Data Mining courses. It introduces key concepts, tools, and techniques for supervised and unsupervised learning, with a focus on computational techniques.
Format: Hardback
Length: 287 pages
Publication date: 30 March 2021
Publisher: Springer Nature Switzerland AG
This comprehensive textbook serves as a valuable resource for students at various stages of their academic journey, from early undergraduate to graduate courses. Its primary aim is to provide a thorough overview of the fundamental principles and techniques essential for modern data analysis. With a specific focus on preparing students for rigorous Machine Learning and Data Mining courses, this book offers a comprehensive introduction to the key conceptual tools required for data analysis. These tools encompass concentration of measure and PAC bounds, cross validation, gradient descent, and principal component analysis, among others.
The text also provides a comprehensive survey of basic techniques in supervised and unsupervised learning, encompassing regression and classification, as well as dimensionality reduction and clustering. The presentation is designed to be accessible and simplified, making it suitable for students with a range of backgrounds, including those with a background in calculus, probability, and linear algebra. Additionally, students who have some familiarity with programming and algorithms will find it useful in understanding advanced topics related to computational techniques.
By providing a comprehensive foundation in data analysis, this textbook equips students with the skills and knowledge necessary to tackle complex data-driven problems and make informed decisions based on empirical evidence. Whether you are a aspiring data scientist, analyst, or researcher, this textbook is an essential resource for advancing your understanding of the field and achieving your academic and professional goals.
Weight: 666g
Dimension: 159 x 242 x 27 (mm)
ISBN-13: 9783030623401
Edition number: 1st ed. 2021
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