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Mathematics for Machine Learning
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Mathematics for Machine Learning Paperback - 2020

by Marc Peter Deisenroth; A. Aldo Faisal; Cheng Soon Ong


From the publisher

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.

Details

  • Title Mathematics for Machine Learning
  • Author Marc Peter Deisenroth; A. Aldo Faisal; Cheng Soon Ong
  • Binding Paperback
  • Pages 398
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Date 2020-04-23
  • ISBN 9781108455145 / 110845514X
  • Weight 1.8 lbs (0.82 kg)
  • Dimensions 9.84 x 6.93 x 0.87 in (24.99 x 17.60 x 2.21 cm)
  • Library of Congress subjects Machine learning - Mathematics
  • Library of Congress Catalog Number 2019040762
  • Dewey Decimal Code 006.31
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