書誌事項

Mathematics for machine learning

Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

Cambridge University Press, 2020

  • : paperback
  • : hardback

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注記

Bibliography: p. 357-366

Includes index

内容説明・目次

内容説明

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.

目次

  • 1. Introduction and motivation
  • 2. Linear algebra
  • 3. Analytic geometry
  • 4. Matrix decompositions
  • 5. Vector calculus
  • 6. Probability and distribution
  • 7. Optimization
  • 8. When models meet data
  • 9. Linear regression
  • 10. Dimensionality reduction with principal component analysis
  • 11. Density estimation with Gaussian mixture models
  • 12. Classification with support vector machines.

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詳細情報

  • NII書誌ID(NCID)
    BB30747516
  • ISBN
    • 9781108455145
    • 9781108470049
  • LCCN
    2019040762
  • 出版国コード
    uk
  • タイトル言語コード
    eng
  • 本文言語コード
    eng
  • 出版地
    Cambridge
  • ページ数/冊数
    xvii, 371 p.
  • 大きさ
    26 cm
  • 分類
  • 件名
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