Monographie

Mathematics for machine learning / Marc Peter Deisenroth,... A. Aldo Faisal,... Cheng Soon Ong,...

Type de contenu
  • Texte
Type de médiation
  • sans médiation
Titre(s)
  • Mathematics for machine learning / Marc Peter Deisenroth,... A. Aldo Faisal,... Cheng Soon Ong,...
Auteur(s)
Autre(s) auteur(s)
Publication
  • Cambridge, UK New York, NY : Cambridge University Press
Date de copyright
  • C 2020
Description matérielle
  • 1 vol. (XVII-371 p.) : ill. en noir et en coul., couv. ill. en coul. ; 26 cm
ISBN
  • 9781108470049
  • 1108470041
  • 9781108455145
  • 110845514X
Autres classifications
  • uyqp
  • uyqm
  • pbt
  • tbj
Classification décimale Dewey
  • 006.31 23
Note sur les bibliographies et les index
  • Bibliogr. p. 357-366. Index
Résumé ou extrait
  • 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.
Sujet - Nom commun
Forme, genre ou caractéristiques physiques
Lien copié.
Build V.5.2.2 - 2ecb916194 (29/04/2026 07:35:08)