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dc.contributor.authorGhahramani, Zoubinen
dc.date.accessioned2015-06-18T08:40:35Z
dc.date.available2015-06-18T08:40:35Z
dc.date.issued2015-05-27en
dc.identifier.citationGhahramani. Nature (2015) 521, 452-459. doi:10.1038/nature14541en
dc.identifier.issn0028-0836
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/248538
dc.description.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.
dc.description.sponsorshipThe 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.
dc.languageEnglishen
dc.language.isoenen
dc.publisherNPG
dc.subjectMathematics and computingen
dc.subjectNeuroscienceen
dc.subjectComputer scienceen
dc.titleProbabilistic machine learning and artificial intelligenceen
dc.typeArticle
dc.description.versionThis is the author accepted manuscript. The final version is available from NPG at http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html#abstract.en
prism.endingPage459
prism.publicationDate2015en
prism.publicationNameNatureen
prism.startingPage452
prism.volume521en
dc.rioxxterms.funderEPSRC
dc.rioxxterms.projectidEP/I036575/1
dcterms.dateAccepted2015-04-21en
rioxxterms.versionofrecord10.1038/nature14541en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2015-05-27en
dc.contributor.orcidGhahramani, Zoubin [0000-0002-7464-6475]
dc.identifier.eissn1476-4687
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idEPSRC (EP/I036575/1)
rioxxterms.freetoread.startdate2015-11-27


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