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Cover: 9781107163447 | Probabilistic Numerics | Computation as Machine Learning | Buch | 2022
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Probabilistic Numerics
Computation as Machine Learning
Buch von Hans P. Kersting (u. a.)
Sprache: Englisch

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Beschreibung
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.
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.
Über den Autor
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).
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2022
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Inhalt: Gebunden
ISBN-13: 9781107163447
ISBN-10: 1107163447
Sprache: Englisch
Einband: Gebunden
Autor: Kersting, Hans P.
Osborne, Michael A.
Hennig, Philipp
Hersteller: Cambridge University Press
Maße: 260 x 213 x 25 mm
Von/Mit: Hans P. Kersting (u. a.)
Erscheinungsdatum: 30.06.2022
Gewicht: 1,15 kg
Artikel-ID: 121318308
Über den Autor
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).
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2022
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Inhalt: Gebunden
ISBN-13: 9781107163447
ISBN-10: 1107163447
Sprache: Englisch
Einband: Gebunden
Autor: Kersting, Hans P.
Osborne, Michael A.
Hennig, Philipp
Hersteller: Cambridge University Press
Maße: 260 x 213 x 25 mm
Von/Mit: Hans P. Kersting (u. a.)
Erscheinungsdatum: 30.06.2022
Gewicht: 1,15 kg
Artikel-ID: 121318308
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