从内核到因果推理][From kernels to causal inference]

从内核到因果推理 From kernels to causal inference


课程网址:	http://videolectures.net/nips2011_scholkopf_inference/
主讲教师:	Bernhard Schölkopf
开课单位:	马克斯普朗克研究所
开课时间:	2012-01-25
课程语种:	英语
中文简介:	机器学习中的核方法已经从用于构建非线性算法的技巧扩展到用于分析更高阶统计量和分布属性的通用工具。他们也在因果推理中找到了应用，这是一个有趣的领域，通过测试他们的概率足迹来检查因果结构。然而，因果推理和现代机器学习之间的联系超出了这个范围，并且讨论将概述一些初步想法，如协变量转换适应和半监督学习等问题如何从因果方法中受益。
课程简介:	Kernel methods in machine learning have expanded from tricks to construct nonlinear algorithms to general tools to assay higher order statistics and properties of distributions. They find applications also in causal inference, an intriguing field that examines causal structures by testing their probabilistic footprints. However, the links between causal inference and modern machine learning go beyond this and the talk will outline some initial thoughts how problems like covariate shift adaptation and semi-supervised learning can benefit from the causal methodology.
关键词:	机器学习; 核方法; 因果推理
课程来源:	视频讲座网
最后编审:	2019-07-26：cwx
阅读次数:	77

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