Binding: Hardcover
Description: 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 Ph D 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.
Title: Probabilistic Numerics Computation As Machine Learning
Author(s): Kersting Hans P. (Ecole Normale Superieure Paris)
Publisher: Cambridge University Press
Barcode: 9781107163447
Pages: 410 Pages
Publication Date: 10/13/2022
Category: Machine Learning