000			02723cam a22003738i 4500
999			_c737 _d737
001			21336577
003			OSt
005			20251027105703.0
008			191130s2020 enk b 001 0 eng
010			_a 2019040762
020			_a9781108455145 _q(paperback)
040			_aLBSOR/DLC _beng _erda _cDLC
042			_apcc
050	0	0	_aQ325.5 _b.D45 2020
082	0	0	_a006.31 _bD325 2020 _223
100	1		_aDeisenroth, Marc Peter, _eauthor.
245	1	0	_aMathematics for machine learning / _cMarc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong.
263			_a1912
264		1	_aCambridge ; _aNew York, NY : _bCambridge University Press, _c2020.
300			_axvii, 371 pages cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction and motivation -- Linear algebra -- Analytic geometry -- Matrix decompositions -- Vector calculus -- Probability and distribution -- Continuous optimization -- When models meet data -- Linear regression -- Dimensionality reduction with principal component analysis -- Density estimation with Gaussian mixture models -- Classification with support vector machines.
520			_a"The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts"-- _cProvided by publisher.
650		0	_aMachine learning _xMathematics.
700	1		_aFaisal, A. Aldo, _eauthor.
700	1		_aOng, Cheng Soon, _eauthor.
776	0	8	_iOnline version: _aDeisenroth, Marc Peter. _tMathematics for machine learning. _dCambridge, United Kingdom ; New York : Cambridge University Press, 2020. _z9781108679930 _w(DLC) 2019040763
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2ddc _cCIR