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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Meilleurs avis pour PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE
Had a wonderful and enriching fun filled experience, Thank you Daphne Ma'am
Very good course. Subject is quiet complex: lack of concrete examples to make sure concepts well understood. Had to review each the Course twice to understand concepts well
Great course! Expect to spend significant time reviewing the material.
Awesome class to gain better understanding of inference for graphical model
À propos du Spécialisation Modèles graphiques probabilistes
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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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