000			01987nam a22003137a 4500
003			OSt
005			20241112124732.0
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020			_a9783030623401
040			_aNISER LIBRARY _beng _cNISER LIBRARY
082			_a004.8 _bPHI-M
100			_aPhillips, Jeff M.
245			_aMathematical foundations for data analysis
260			_aSwitzerland : _bSpringer, _c2021
300			_axvii, 287p.
490			_aSpringer Series in the Data Sciences
504			_aIncludes bibliographical references and index
520			_aThis 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.
650			_aData mining _xMathematics
650			_aMachine learning _xMathematics
650			_aData analysis
650			_aNeural networks
650			_aData sciences
650			_aDimensionality reduction
650			_aBig data
856			_3Table of contents _uhttps://link.springer.com/content/pdf/bfm:978-3-030-62341-8/1
856			_3Reviews _uhttps://www.goodreads.com/book/show/57190260-mathematical-foundations-for-data-analysis?ref=nav_sb_ss_1_13#CommunityReviews
942			_2udc _cBK
999			_c35374 _d35374