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Probabilistic Graphical Models 1: Representation, Université de Stanford

4.7
965 notes
219 avis

À propos de ce cours

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....

Meilleurs avis

par 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!!

par 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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212 avis

par Marno Basson

Feb 03, 2019

Absolutely love it!!!!

:)

par 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.

par Ben LI

Jan 13, 2019

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

par Lik Ming Cheong

Jan 12, 2019

A great course! The provided training clarifies all key concepts

par Utkarsh Agrawal

Dec 30, 2018

maza aa gaya

par Myoungsu Choi

Dec 26, 2018

Writing on the ppt is not clear to see.

par Xiaojie Zhang

Dec 22, 2018

Some interesting knowledges about PCM, but I think I need more detailed information in the succeeding courses.

par Alexandru Iftimie

Nov 25, 2018

Great course. Interesting concepts to learn, but some of them are too quickly and poorly explained.

par 张浩悦

Nov 22, 2018

funny!!

par Larry Lyu

Nov 18, 2018

This course seems to have been abandoned by Coursera. Mentors never reply to discussion forum posts (if there is any active mentor at all). Many assignments and tests are confusing and misleading. There are numerous materials you can find online to learn about Graphical Models than spending time & money on this.