The main goal of this article is to convince you, the reader, that supervised learning in the Probably Approximately Correct (PAC) model is closely related to -- of all things -- bipartite matching! En-route from PAC learning to bipartite matching, I will overview a particular transductive model of learning, and associated one-inclusion graphs, which can be viewed as a generalization of some of the hat puzzles that are popular in recreational mathematics. Whereas this transductive model is far from new, it has recently seen a resurgence of interest as a tool for tackling deep questions in learning theory. A secondary purpose of this article could be as a (biased) tutorial on the connections between the PAC and transductive models of learning.

翻译：本文的主要目标是说服读者，监督学习中的可能近似正确（PAC）模型与二分图匹配密切相关！在从PAC学习到二分图匹配的探讨过程中，我将概述一种特定的转导学习模型及其关联的单包含图——该模型可视为娱乐数学中流行的帽子谜题之推广。尽管这一转导模型并非全新概念，但近期它作为解决学习理论深层问题的工具重新引起了广泛关注。本文的次要目标亦可视为对PAC学习模型与转导学习模型关联性的（带有倾向性的）专题解析。