Mathematics for Machine Learning
By:Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
Published on 2020-04-23 by Cambridge University Press
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.
This Book was ranked at 3 by Google Books for keyword Math book.
Book ID of Mathematics for Machine Learning's Books is t4XQDwAAQBAJ, Book which was written byMarc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Onghave ETAG "H8ex0YIc0Yc"
Book which was published by Cambridge University Press since 2020-04-23 have ISBNs, ISBN 13 Code is 9781108569323 and ISBN 10 Code is 1108569323
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