國立虎尾科技大學 |

Probabilistic numerics : = computation as machine learning /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Probabilistic numerics :/ Philipp Hennig, Michael A. Osborne, Hans P. Kersting.
Reminder of title:	computation as machine learning /
Author:	Hennig, Philipp,
other author:	Osborne, Michael A.,
Description:	1 online resource (xii, 398 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 10 Jun 2022).
Subject:	Machine learning - Mathematics. -
Online resource:	https://doi.org/10.1017/9781316681411
ISBN:	9781316681411 (ebook)

Probabilistic numerics : = computation as machine learning /
Hennig, Philipp,

Probabilistic numerics :computation as machine learning /Philipp Hennig, Michael A. Osborne, Hans P. Kersting. - 1 online resource (xii, 398 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 10 Jun 2022).

Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.

ISBN: 9781316681411 (ebook)Subjects--Topical Terms:

1340126
Machine learning
--Mathematics.

LC Class. No.: Q325.5 / .H46 2022

Dewey Class. No.: 006.31

Probabilistic numerics : = computation as machine learning /
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520 $a Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.
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