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.
- 5 stars74.71%
- 4 stars17.78%
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來自PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION的熱門評論
Great course. some programming assignments are tough (not too nicely worded and automatic grader can be a bit annoying) but all in all, great course
This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.
Some parts are challenging enough in the PAs, if you are familiar with Matlab this course is a great opportunity to get familiar with PGMs and learn to handle these.
Prof. Koller did a great job communicating difficult material in an accessible manner. Thanks to her for starting Coursera and offering this advanced course so that we can all learn...Kudos!!
關於 概率图模型 專項課程
Learning Outcomes: By the end of this course, you will be able to