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 3: LEARNING的热门评论
An amazing course! The assignments and quizzes can be insanely difficult espceially towards the conclusion.. Requires textbook reading and relistening to lectures to gather the nuances.
Great course! Very informative course videos and challenging yet rewarding programming assignments. Hope that the mentors can be more helpful in timely responding for questions.
Great course! It is pretty difficult - be prepared to study. Leave plenty of time before the final exam.
Amazing! This is the first specialization that I have finished and it feels amazing! Daphne was amazing!
关于 概率图模型 专项课程
Learning Outcomes: By the end of this course, you will be able to