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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來自PROBABILISTIC GRAPHICAL MODELS 3: LEARNING的熱門評論
Plz give practical assignments in Python. Matlab is not free and not many and neither myself know Matlab.
A great course! Learned a lot. Especially the assignments are excellent! Thanks a lot.
Excellent course. Assignments are challenging but once you figure them out you will have a solid understanding of PGM.
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.
關於 概率图模型 專項課程
Learning Outcomes: By the end of this course, you will be able to