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Marc Peter Deisenroth

MATHEMATICS FOR MACHINE LEARNING

MATHEMATICS FOR MACHINE LEARNING

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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. Read more

Book Details

Author: Marc Peter Deisenroth

Publisher: ‎ Cambridge University Press (23 April 2020); KDPUBLICATION

Format: Paperback – 23 April 2020

Language: ‎ English

Pages: 398

ISBN: ‎ 978-1108455145

Dimensions: ‎ 17.78 x 2.24 x 25.4 cm

Weight: ‎ 839 g

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  • Book Publisher: ‎ Cambridge University Press (23 April 2020); KDPUBLICATION

    Book Binding : Paperback – 23 April 2020

  • Book Language : ‎ English

    Item ISBN-13 :

  • Book Dimension: ‎ 839 g

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MATHEMATICS FOR MACHINE LEARNING
MATHEMATICS FOR MACHINE LEARNING
MATHEMATICS FOR MACHINE LEARNING
Regular price
Rs. 2,999.00
Sale price
Rs. 2,499.00/ea
Rs. 0.00
Regular price
Rs. 2,999.00
Sale price
Rs. 2,499.00/ea
Rs. 0.00