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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Top-Bewertungen von PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE
Great course. The assignments are old and are not worth doing it. But the content is good for those who are interested in Probabilistic Graphical Models basics.
Great introduction to inference. Requires some extra reading from the textbook.
Great balance between theories and practices. Also provide a lot of intuitions to understand the concepts
Just like the first course of the specialization, this course is really good. It is well organized and taught in the best way which really helped me to implement similar ideas for my projects.
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Learning Outcomes: By the end of this course, you will be able to take a given PGM and
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