Causality for ML
信息论视角下因果融合机器学习
信息论视角下因果融合机器学习
The paper Causality for Machine Learning is very comprehensive, delightful and inspiring. It should be recommended to ALL, not just MANY ML/AL folks.
Bernhard Schölkopf is a director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, where he heads the Department of Empirical Inference. He is a leading researcher in the machine learning community, where he is particularly active in the field of kernel methods.
He wrote the book Elements of Causal Inference: Foundations and Learning Algorithms.
Contents of the paper Causality for Machine Learning
CS 7290 Special Topics in Data Science Summer 2019 Prof. Robert Osazuwa Ness Northeastern University, Khoury College of Computer Sciences
This course targets data scientists and ML engineers familiar with probabilistic generative modeling in machine learning. Learners familiar with how to use a tensor-based framework to build a Gaussian mixture model or a variational auto-encoder will find the material grafts directly onto this modeling intuition. This course focuses on causal probabilistic modeling and structural causal models because they fit nicely into that generative model framework and its toolsets. The course also covers elements of the Neyman–Rubin causal model that are commonplace in professional machine learning settings.
近日,马克斯·普朗克智能系统中心主任Bernhard Schölkopf 发表论文,谈论了因果关系和机器学习之间的联系,并科普了一些相关的重要概念。Judea Pearl 转发相关推文,表示”这是一篇非常全面、令人愉悦且极具启发性的论文,适合所有人,而不仅仅是机器学习以及人工智能从业者阅读。” 那么这篇文章的最大启发在哪里呢?下面是龚鹤扬和郭瑞东对该论文关于信息革命的内容,也就是论文中的第二个章节,进行翻译和解读。该章节最具备启发性,而其他章节是有关因果理论框架以及因果思维如何融入机器学习的具体介绍。论文突出了信息的根基性地位,指出信息就是工业革命的能源,启示了因果理论和机器学习的研究应该要融入信息的视角。