000			02581nam a22002657a 4500
003			OSt
005			20260610095755.0
008			260610b |||||||| |||| 00| 0 eng d
020			_a9781009108850
041			_aeng
082			_a006.31 DEI-M
100			_aDeisenroth, Marc Peter. _930834
245			_aMathematics for machine learning
260			_aCambridge : _bCambridge University Press, _c2026
300			_axvii, 371p.
440			_aMachine learning _vMathematics _983637
440			_aArtificial intelligence. _983638
505			_a1. Introduction and motivation 2. Linear algebra 3. Analytic geometry 4. Matrix decompositions 5. Vector calculus 6. Probability and distribution 7. Optimization 8. When models meet data 9. Linear regression 10. Dimensionality reduction with principal component analysis 11. Density estimation with Gaussian mixture models 12. Classification with support vector machines.
520			_ahe 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. A one-stop presentation of all the mathematical background needed for machine learning Worked examples make it easier to understand the theory and build both practical experience and intuition Explains central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines
700			_aFaisal, A. Aldo. _983639
700			_aOng, Cheng Soon. _983640
856			_uhttps://www.cambridge.org/9781108470049
942			_cBK
999			_c200712 _d200712