Probabilistic Numerics: Computation as Machine Learning - Rilegato

Hennig, Philipp; Osborne, Michael A.; Kersting, Hans P.

 
9781107163447: Probabilistic Numerics: Computation as Machine Learning
Rilegato
ISBN 10: 1107163447 ISBN 13: 9781107163447
Casa editrice: Cambridge University Press, 2022

Sinossi

A thorough introduction to probabilistic numerics showing how to build more flexible, efficient, or customised algorithms for computation.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sugli autori

Philipp Hennig holds the Chair for the Methods of Machine Learning at the University of Tübingen, and an adjunct position at the Max Planck Institute for Intelligent Systems. He has dedicated most of his career to the development of Probabilistic Numerical Methods. Hennig's research has been supported by Emmy Noether, Max Planck and ERC fellowships. He is a co-Director of the Research Program for the Theory, Algorithms and Computations of Learning Machines at the European Laboratory for Learning and Intelligent Systems (ELLIS).

Michael A. Osborne is Professor of Machine Learning at the University of Oxford, and a co-Founder of Mind Foundry Ltd. His research has attracted £10.6M of research funding and has been cited over 15,000 times. He is very, very Bayesian.

Hans P. Kersting is a postdoctoral researcher at INRIA and École Normale Supérieure in Paris, working in machine learning with expertise in Bayesian inference, dynamical systems, and optimisation.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Cambridge University Press
Data di pubblicazione
2022
Lingua
Inglese
ISBN 10 
1107163447
ISBN 13 
9781107163447
Rilegatura
Copertina rigida
Numero di pagine
410
Contatto del produttore
non disponibile
Persona responsabile
Avery
https://www.avery.fr/

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