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.
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来自PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE的热门评论
Amazing course! Loved how Daphne explained very complicated things in an understandable manner!
Had a wonderful and enriching fun filled experience, Thank you Daphne Ma'am
It would be great to have more examples included in the lectures and slides.
Great balance between theories and practices. Also provide a lot of intuitions to understand the concepts
关于 概率图模型 专项课程
Learning Outcomes: By the end of this course, you will be able to take a given PGM and