TY - BOOK AU - Deisenroth,Marc Peter AU - Faisal,A.Aldo AU - Ong,Cheng Soon TI - Mathematics for machine learning SN - 9781108470049 AV - Q325.5 .D45 2020 U1 - 006.3/1 D325m 2019 23 PY - 2019/// CY - Cambridge PB - Cambridge University Press KW - Machine learning KW - Mathematics N1 - Includes bibliographical references and index; Introduction and motivation -- Linear algebra -- Analytic geometry -- Matrix decompositions -- Vector calculus -- Probability and distribution -- Continuous optimization -- When models meet data -- Linear regression -- Dimensionality reduction with principal component analysis -- Density estimation with Gaussian mixture models -- Classification with support vector machines; Mathematics N2 - "The 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"-- ER -