TY  - BOOK
AU  - Phillips, Jeff M.
TI  - Mathematical foundations for data analysis
T2  - Springer Series in the Data Sciences 
SN  - 9783030623401
U1  - 004.8 
PY  - 2021///
CY  - Switzerland
PB  - Springer
KW  - Data mining
KW  - Mathematics
KW  - Machine learning
KW  - Data analysis
KW  - Neural networks
KW  - Data sciences
KW  - Dimensionality reduction
KW  - Big data
N1  - Includes bibliographical references and index
N2  - 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
UR  - https://link.springer.com/content/pdf/bfm:978-3-030-62341-8/1
UR  - https://www.goodreads.com/book/show/57190260-mathematical-foundations-for-data-analysis?ref=nav_sb_ss_1_13#CommunityReviews
ER  -