返回到 Probabilistic Graphical Models 1: Representation

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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.
This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly....

創建者 ST

•Jul 13, 2017

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!!

創建者 CM

•Oct 23, 2017

The course was deep, and well-taught. This is not a spoon-feeding course like some others. The only downside were some "mechanical" problems (e.g. code submission didn't work for me).

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218 個審閱

創建者 Phillip Wenig

•Apr 08, 2019

Sometimes the questions weren't clear. But in general, I really like the course and the things I've learnt I am sure they are useful.

創建者 Alexander Perusse

•Apr 02, 2019

I really enjoyed the content of this course. Having been inspired by reading The Book of Why, I was looking for some formal language around Bayesian Networks and this course really fit the bill. My biggest piece of feedback is on the programming assignments. These really should be in Python. Octave is an okay choice, and I suspect might have to do with Andrew Ng original choice to use it for his own machine learning course. However, the data science community writ large uses Python and R, which is why Andrew switched to Python for his deep learning courses. I would recommend the programming assignment be updated so that they are more accessible to the data science community.

創建者 Isaiah Onando Mulang'

•Mar 31, 2019

I found well structured contend of these rare probabilistic methods (Actually this is the only reasonable course in this approach online)

創建者 杨涛

•Mar 27, 2019

I think this course is quite useful for my own research, thanks Cousera for providing such a great course.

創建者 胡声鼎

•Mar 10, 2019

A very nice-designed course

創建者 Mahmoud Shepero

•Feb 25, 2019

Very good explanation and excellent assignments

創建者 Marno Basson

•Feb 03, 2019

Absolutely love it!!!!

:)

創建者 Lorenzo Battarra

•Jan 19, 2019

The course contents are presented very clearly. Difficult ideas are conveyed in a precise and convincing way. Despite this, the global structure is not presented very clearly, and the quality of some course material is not excellent. In particular, I didn't find the optional programming assignments particularly interesting, and the code/questions contained more than one bug. Also, the quality of video/sound is quite poor, and varies a lot from course to course.

創建者 Ben LI

•Jan 13, 2019

Would be better if there are people monitoring the discussion board and actually answer student's questions.

創建者 Lik Ming Cheong

•Jan 12, 2019

A great course! The provided training clarifies all key concepts