000			01587 a2200181 4500
005			20250804180213.0
020			_a9781108455145
082			_a006.31 DEI
100			_aDeisenroth, Marc Peter
245			_aMathematics For Machine learning
260			_bCambridge University Press _c2020 _aNew York
300			_a371
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. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
650			_aMachine Learning - Mathematics
700			_aFaisal, A. Aldo
700			_aOng, Cheng Soon
942			_cBK _2ddc
999			_c51008 _d51008