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 stars71.52%
- 4 stars19.66%
- 3 stars5.42%
- 2 stars2.71%
- 1 star0.67%
PROBABILISTIC GRAPHICAL MODELS 3: LEARNING からの人気レビュー
Great course, though with the progress of ML/DL, content seems a touch outdated. Would
Great course! Very informative course videos and challenging yet rewarding programming assignments. Hope that the mentors can be more helpful in timely responding for questions.
very good course for PGM learning and concept for machine learning programming. Just some description for quiz of final exam is somehow unclear, which lead to a little bit confusing.
1) The fórums need better assistance.
2) If we could submit Python code por the homework assignments, that would be much better for me.
Learning Outcomes: By the end of this course, you will be able to