Probabilistic Numerics: Computation as Machine Learning - Hardcover

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

Hardcover

ISBN 10: 1107163447 ISBN 13: 9781107163447

Publisher: Cambridge University Press, 2022

Synopsis

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

About this title

About the Authors

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.

Edition details

• Publisher: Cambridge University Press
• Publication date: 2022
• Language: English
• ISBN 10: 1107163447
• ISBN 13: 9781107163447
• Binding: Hardcover
• Number of pages: 410