000			02135cam a22002658i 4500
003			RNL
005			20260107094444.0
008			191130s2020 enk b 001 0 eng
020			_a9781108470049
020			_a9781108455145
040			_aRCL
082	0	0	_a006.31 D27M
100	1		_aDeisenroth, Marc Peter, _930039
245	1	0	_aMathematics for Machine Learning _c/Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
260			_aNew York: _bCambridge University Press, _c2020.
300			_axvii, 371p.
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"--
546			_aEnglish
650		0	_aMachine learning _926348
700	1		_aFaisal, A. Aldo, _930040
700	1		_aOng, Cheng Soon, _930041
942			_cBK
999			_c47571 _d47571