01768nam a22002057a 4500 20260211143946.0 260211b |||||||| |||| 00| 0 eng d 9781108455145 AL 006.31 DEIM Marc Peter Deisenroth and others 254460 Mathematics for machine learning New York Cambridge University Press 2020 xvii,371p PB 25.5x17.5cm Computer Science Engineering IN-2535 ₹1625.25 ₹ ₹2749.50 0% 27-01-2026 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. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site. Faisal, A Aldo 254415 Ong, Cheng Soon 254416 ddc BK 240881 240881 0 0 ddc 0 0 CSE SE SE 2026-01-27 amazon.in 1625.10 1 006.31 DEIM SE000202 2026-05-21 2026-05-11 13:12:20 2026-05-11 https://mml-book.github.io/book/mml-book.pdf 2749.50 2026-01-27 BK 0 0 ddc 0 0 CSE SE SE 2026-01-27 amazon.in 1625.15 0 006.31 DEIM SE000203 2026-02-11 09:51:15 https://mml-book.github.io/book/mml-book.pdf 2749.50 2026-01-27 BK