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.
- 5 stars71.62%
- 4 stars19.59%
- 3 stars5.40%
- 2 stars2.70%
- 1 star0.67%
來自PROBABILISTIC GRAPHICAL MODELS 3: LEARNING的熱門評論
Great course, though with the progress of ML/DL, content seems a touch outdated. Would
1) The fórums need better assistance.
2) If we could submit Python code por the homework assignments, that would be much better for me.
A great course! Learned a lot. Especially the assignments are excellent! Thanks a lot.
Amazing! This is the first specialization that I have finished and it feels amazing! Daphne was amazing!
關於 概率图模型 專項課程
Learning Outcomes: By the end of this course, you will be able to