000			nam a22 4500
999			_c32045 _d32045
008			230903b xxu||||| |||| 00| 0 eng d
020			_a9781108455145
082			_a006.31 _bDEI
100			_aDeisenroth, Marc Peter
245			_aMathematics for machine learning
260			_bCambridge University Press, _c2020 _aCambridge :
300			_axvii, 371 p. ; _bill., _c26 cm
365			_b37.99 _cGBP _d110.40
504			_aIncludes bibliographical references and index.
520			_aThe 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.
650			_aMachine learning
650			_aMathematics
700			_aFaisal, A. Aldo
700			_aOng, Cheng Soon
942			_2ddc _cBK