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Mathematics for Machine Learning

Marc Peter Deisenroth, University College London, A. Aldo Faisal, Imperial College London, Cheng Soon Ong, Data61, CSIRO

Description

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…

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Key features

  • A one-stop presentation of all the mathematical background needed for machine learning
  • Worked examples make it easier to understand the theory and build both practical experience and intuition
  • Explains central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines

About the book

  • DOI https://doi.org/10.1017/9781108679930
  • Subjects Computational Science,Computer Science,Machine Learning and Pattern Recognition,Mathematics
  • Format: Hardback
    • Publication date: 23 April 2020
    • ISBN: 9781108470049
    • Dimensions (mm): 253 x 177 mm
    • Weight: 0.95g
    • Contains: 3 b/w illus. 106 colour illus.
    • Page extent: 398 pages
    • Availability: Manufactured on demand: supplied direct from the printer
  • Format: Paperback
    • Publication date: 23 April 2020
    • ISBN: 9781108455145
    • Dimensions (mm): 253 x 177 mm
    • Weight: 0.8g
    • Contains: 3 b/w illus. 106 colour illus.
    • Page extent: 398 pages
    • Availability: In stock
  • Format: Digital
    • Publication date: 20 February 2020
    • ISBN: 9781108679930

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