000			a
003			OSt
005			20250729231929.0
008			250723b xxu||||| |||| 00| 0 eng d
020			_a9781470435752 _c(hbk)
040			_cKB
082			_a510 _bMAH
100			_aMahoney, Michael W. _eed.
245			_aMathematics of data
260			_bAmerican Mathematical Society, _c2018 _aProvidence :
300			_axii, 325 p. : _bill. ; _c26 cm.
365			_b125.00 _c$ _d85.58
490			_aIAS/​Park City mathematics series ; _vv.25
504			_aIncludes bibliographical references.
520			_aData science is a highly interdisciplinary field, incorporating ideas from applied mathematics, statistics, probability, and computer science, as well as many other areas. This book gives an introduction to the mathematical methods that form the foundations of machine learning and data science, presented by leading experts in computer science, statistics, and applied mathematics. Although the chapters can be read independently, they are designed to be read together as they lay out algorithmic, statistical, and numerical approaches in diverse but complementary ways.
650			_aBig data
650			_aMathematics
650			_aMathematics teachers
650			_aLinear and multilinear algebra
650			_aMatrix theory
650			_aResearch exposition
650			_aConvex and discrete geometry
650			_aProbability theory
650			_aStochastic processes
650			_aStatistics
650			_aNumerical analysis
650			_aComputer science
650			_aOperations research
650			_aMathematical programming1
700			_aDuchi, John C. _eed.
942			_2ddc _cBK
999			_c34097 _d34097