开始时间: 01/12/2019 持续时间: Unknown
Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.
斯坦福大学 Daphne Koller 教授的概率图模型专项课程系列（Probabilistic Graphical Models Specialization），作为Coursera上最早的几门课程之一，这门概率图模型课程的相当有难度，完成作业拿到证书还是很有挑战的，感兴趣的同学可以关注：Probabilistic Graphical Models-Master a new way of reasoning and learning in complex domains