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 stars74.69%
- 4 stars17.79%
- 3 stars5.21%
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- 1 star1.28%
來自PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION的熱門評論
I really enjoyed attending this course. It is foundational material for anyone who wants to use graphical models for inference and decision making..
The lecture was a bit too compact and unsystematic. However, if you also do a lot of reading of the textbook, you can learn a lot. Besides, the Quiz and Programming task are of high qualities.
This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.
Great content and easy to pick up. Only issue was with downloaded Octave software. Does not work, despite multiple downloads on different machines
關於 概率图模型 專項課程
Learning Outcomes: By the end of this course, you will be able to