01720nam a2200217Ia 4500 TDMU_41689 210410s9999 xx 000 0 und d 781.25 D201 Deisenroth, Marc Peter Mathematics for machine learning Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong Cambridge ;New York, NY Cambridge University Press 2020 xvii,371p. "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" Machine learning Mathematics; Toán học Ứng dụng Aldo Faisal, A http://lrc.tdmu.edu.vn/opac/search/detail.asp?aID=2&ID=41689 Trung tâm Học liệu Trường Đại học Thủ Dầu Một