人工智能
概率逻辑
计算机科学
机器学习
领域(数学)
校长(计算机安全)
贝叶斯概率
统计模型
认知机器人学
机器人
数学
操作系统
纯数学
出处
期刊:Nature
[Springer Nature]
日期:2015-05-27
卷期号:521 (7553): 452-459
被引量:1500
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.
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