000			01707 a2200181 4500
020			_a9781108455145
082			_a006.3/1 _bDEI-M
100			_aDeisenroth, Marc Peter,
245		0	_aMathematics for Machine Learning
260			_aNew York _bCambridge University, _c2020
300			_axvii, 371 _c17.78 x 2.24 x 25.4 cm
500			_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.
504			_aIncluded with refence and Index
650			_aComputer Science, Mathematics, Machine Learning, Mathematics, Foundation, Linear Algebra, Analytics Geometry
700			_aFaisal, Aldo
700			_aOng, Cheng Soon
942			_cBK
999			_c28278 _d28278