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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Habilidades que obtendrás
- 5 stars74,73 %
- 4 stars17,77 %
- 3 stars5,21 %
- 2 stars0,99 %
- 1 star1,28 %
Principales reseñas sobre PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION
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
Overall very good quality content. PAs are useful but some questions/tests leave too much to interpretation and can be frustrating for students. Audio quality for the classes could also be improved.
Prof. Koller did a great job communicating difficult material in an accessible manner. Thanks to her for starting Coursera and offering this advanced course so that we can all learn...Kudos!!
Acerca de Programa especializado: modelos gráficos de probabilidades
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Learning Outcomes: By the end of this course, you will be able to
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