000 | nam a22 7a 4500 | ||
---|---|---|---|
999 |
_c29582 _d29582 |
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008 | 190625b xxu||||| |||| 00| 0 eng d | ||
020 |
_a9781470435752 _c(hbk) |
||
082 |
_a510 _bMAH |
||
100 |
_a Mahoney, Michael W. _eedt |
||
245 | _aMathematics of Data | ||
260 |
_aUSA : _bAmerican Mathematical Society, _c2018 |
||
300 |
_axii, 325 p. : _bill. ; _c26 cm. |
||
365 |
_b104.00 _cUSD _d00 |
||
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 programming | ||
700 |
_aDuchi, John C. _eedt |
||
700 |
_aGilbert, Anna C. _eedt |
||
942 |
_2ddc _cBK |