Mathematical Foundations for Data Analysis

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: Paperback / softback
Length: 287 pages
Publication date: 31 March 2022
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.

Furthermore, the textbook provides a comprehensive survey of basic techniques in supervised and unsupervised learning, presented in an accessible and simplified manner. It covers topics such as regression and classification, dimensionality reduction, and clustering, ensuring that students have a solid foundation in these areas.

To fully benefit from this textbook, students are advised to have a solid background in calculus, probability, and linear algebra. Additionally, some familiarity with programming and algorithms is highly beneficial, as it enables students to grasp advanced topics related to computational techniques.

By presenting the material in a clear and concise manner, this textbook ensures that students can effectively understand and apply the concepts discussed. It serves as a valuable companion for anyone seeking to excel in the field of data analysis and prepares them for the challenges and opportunities that lie ahead in this rapidly evolving field.

Weight: 474g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030623432
Edition number: 1st ed. 2021