Format

Send to

Choose Destination
See comment in PubMed Commons below
Nature. 2015 May 28;521(7553):452-9. doi: 10.1038/nature14541.

Probabilistic machine learning and artificial intelligence.

Author information

1
Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK.

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.

PMID:
26017444
DOI:
10.1038/nature14541
[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Nature Publishing Group
    Loading ...
    Support Center