Probabilistic machine learning and artificial intelligence
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Ghahramani, Z. (2015). Probabilistic machine learning and artificial intelligence. Nature, 521 452-459. https://doi.org/10.1038/nature14541
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.
Mathematics and computing, Neuroscience, Computer science
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.
External DOI: https://doi.org/10.1038/nature14541
This record's URL: https://www.repository.cam.ac.uk/handle/1810/248538