000			02049cam a22003258i 4500
999			_c2010 _d2010
003			OSt
005			20230414151714.0
008			191130s2020 enk b 001 0 eng
020			_a9781108470049 _q(hardback)
020			_a9781108455145 _q(paperback)
020			_z9781108679930 _q(epub)
040			_aLBSOR/DLC _beng _erda _cDLC
100	1		_aDeisenroth, Marc Peter _96994
100	1		_aFaisal, A. Aldo _96996
100	1		_aOng, Cheng Soon _96997
245	1	0	_aMathematics for machine learning
263			_a1912
264		1	_aCambridge ; _aNew York, NY : _bCambridge University Press, _c2020.
300			_apages cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references and index.
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. _96995
856			_3Book Home _uhttps://mml-book.github.io/
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2JEL _cBO