• Zoubin Ghahramani.
• Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK.
• Nature. 2015 May 28; 521 (7553): 452-9.

AbstractHow 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.

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