Mathematical foundations for data analysis
Phillips, Jeff M.
Mathematical foundations for data analysis - Switzerland : Springer, 2021 - xvii, 287p. - Springer Series in the Data Sciences .
Includes bibliographical references and index
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.
9783030623401
Data mining--Mathematics
Machine learning--Mathematics
Data analysis
Neural networks
Data sciences
Dimensionality reduction
Big data
004.8 / PHI-M
Mathematical foundations for data analysis - Switzerland : Springer, 2021 - xvii, 287p. - Springer Series in the Data Sciences .
Includes bibliographical references and index
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.
9783030623401
Data mining--Mathematics
Machine learning--Mathematics
Data analysis
Neural networks
Data sciences
Dimensionality reduction
Big data
004.8 / PHI-M
