Philipp Hennig

Probabilistic Numerics (eBook, PDF)

Computation as Machine Learning

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 30. Juni 2022
• Englisch
• ISBN-13: 9781316730331
• Artikelnr.: 66231685

Autorenporträt

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).

Inhaltsangabe

Introduction
1. Mathematical background
2. Integration
3. Linear algebra
4. Local optimisation
5. Global optimisation
6. Solving ordinary differential equations
7. The frontier
Solutions to exercises
References
Index.