Probabilistic machine learning and artificial intelligence

被引:1329
作者
Ghahramani, Zoubin [1 ]
机构
[1] Univ Cambridge, Dept Engn, Cambridge CB2 1PZ, England
基金
英国工程与自然科学研究理事会;
关键词
MONTE-CARLO; STATISTICS; MODELS; ROBUST;
D O I
10.1038/nature14541
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页码:452 / 459
页数:8
相关论文
共 100 条
[1]  
Adams R., 2010, INT C ART INT STAT
[2]  
[Anonymous], P 29 AAAI C ART INT
[3]  
[Anonymous], PREPRINT
[4]  
[Anonymous], 2006, Intelligent Robotics and Autonomous Agents series
[5]  
[Anonymous], 2021, Artificial Intelligence: A Modern Approach
[6]  
[Anonymous], 2014, 2 INT C LEARN REPR I
[7]  
[Anonymous], P UNC ART INT
[8]  
[Anonymous], 1961, ALGEBRA PROBABLE INF
[9]  
[Anonymous], 2017, ACM, DOI [DOI 10.2165/00129785-200404040-00005, DOI 10.1145/3065386]
[10]  
[Anonymous], 2018, User's Guid. Ref. Man, P1