rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Mathematics for machine learning
  name:
    name part: Deisenroth, Marc Peter
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Faisal, A. Aldo
    role:
      role term: author.
  name:
    name part: Ong, Cheng Soon
    role:
      role term: author.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: enk
    date issued: 2020
    copyright date: 2020
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: xvii, 371 pages : illustrations (some color) ; 26 cm.
  abstract: 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.
  table of contents: Introduction and motivation -- Linear algebra -- Analytic geometry -- Matrix decompositions -- Vector calculus -- Probability and distribution -- Continuous optimization -- When models meet data -- Linear regression -- Dimensionality reduction with principal component analysis -- Density estimation with Gaussian mixture models -- Classification with support vector machines.
  note: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong.
  note: Includes bibliographical references (pages 357-366) and index.
  subject:
    topic: Machine learning
    topic: Mathematics
  classification: Q325.5 .D45 2020
  classification: 006.3/1
  identifier: 9781108470049
  identifier: 1108470041
  identifier: 9781108455145
  identifier: 110845514X
  identifier: 9781108679930
  identifier: 2019040762
  record info:
    record content source: DLC
    record creation date: 191130
    record change date: 20210125090118.0
    record identifier: in00004286496