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Probabilistic machine learning and artificial intelligence.


Type

Article

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Abstract

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Description

Keywords

Artificial Intelligence, Automation, Bayes Theorem, Data Compression, Models, Statistical, Statistics, Nonparametric, Uncertainty

Journal Title

Nature

Conference Name

Journal ISSN

0028-0836
1476-4687

Volume Title

521

Publisher

Springer Science and Business Media LLC
Sponsorship
Engineering and Physical Sciences Research Council (EP/I036575/1)
The author acknowledges an EPSRC grant EP/I036575/1, the DARPA PPAML programme, a Google Focused Research Award for the Automatic Statistician and support from Microsoft Research.