CUED Publications database

Probabilistic machine learning and artificial intelligence.

Ghahramani, Z (2015) Probabilistic machine learning and artificial intelligence. Nature, 521. pp. 452-459.

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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.

Item Type: Article
Uncontrolled Keywords: Artificial Intelligence Automation Bayes Theorem Data Compression Models, Statistical Statistics, Nonparametric Uncertainty
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 18:57
Last Modified: 21 Nov 2017 03:10
DOI: