000			02300 a2200289 4500
999			_c5798 _d5798
005			20250904141111.0
008			250904b ||||| |||| 00| 0 eng d
020			_a9781108455145
041			_aeng
082			_a006.31 DEI/M
100			_aDeisenroth, Marc Peter _914601
245			_aMathematics for machine learning
250			_a1st/ 2020
260			_bCambridge University Press _aCambridge _cc2020
300			_axvii, 371p.; 25cm.
500			_a Table of Contents 1. 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			_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.
650			_a Computer science _91296
650			_aPattern recognition _914602
650			_aMachine learning _91055
650			_aArtificial intelligence _91643
650			_aMathematics _914603
700			_aFaisal, A. Aldo _914604
700			_aOng, Cheng Soon _914605
856			_uhttps://doi.org/10.1017/9781108679930
942			_cBK