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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PROBABILISTIC GRAPHICAL MODELS 3: LEARNING からの人気レビュー
Excellent course. Assignments are challenging but once you figure them out you will have a solid understanding of PGM.
Great course, though with the progress of ML/DL, content seems a touch outdated. Would
Plz give practical assignments in Python. Matlab is not free and not many and neither myself know Matlab.
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.
Learning Outcomes: By the end of this course, you will be able to