01689nam a22001817a 450000500170000000800410001702000180005804000070007608200170008310000370010024500370013726000470017430000310022136500870025252011290033970000190146870000200148720260211143946.0260211b        |||||||| |||| 00| 0 eng d  a9781108455145  cAL  a006.31bDEIM  aMarc Peter Deisenroth and others  aMathematics for machine learning  aNew YorkbCambridge University Pressc2020  axvii,371pbPBc25.5x17.5cm  2Computer Science EngineeringaIN-2535b₹1625.25c₹d₹2749.50e0%f27-01-2026  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.  aFaisal, A Aldo  aOng, Cheng Soon