[Coursera] Probabilistic Graphical Models

In this class, you will learn the basics of the PGM representation and how to construct them, using both human knowledge and machine learning techniques. Uncertainty is unavoidable in real-world applications: we can almost never predict with certainty what will happen in the future, and even in the present and the past, many important aspects of the world are not observed with certainty. Probability theory gives us the basic foundation to model our beliefs about the different possible states of the world, and to update these beliefs as new evidence is obtained. These beliefs can be combined with individual preferences to help guide our actions, and even in selecting which observations to make. While probability theory has existed since the 17th century, our ability to use it effectively on large problems involving many inter-related variables is fairly recent, and is due largely to the development of a framework known as Probabilistic Graphical Models (PGMs). This framework, which span

在本课程中，您将学习概率图模型（PGM）的基本原理及其构建方法，这包括运用人类知识与机器学习技术。在现实世界的应用中，不确定性是不可避免的：我们几乎无法对未来发生的事情作出确定的预测，甚至在现在和过去，世界的重要方面也难以被确凿观察。概率论为我们提供了一个基础，用以模拟关于世界不同可能状态的信念，并在获取新证据时更新这些信念。这些信念可以与个人偏好相结合，以指导我们的行动，甚至在选择观测哪些现象时发挥作用。尽管概率论自17世纪以来便已存在，但我们在涉及众多相互关联变量的大规模问题中有效运用其能力的提升，主要得益于概率图模型（PGMs）这一框架的发展。该框架，横跨...