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Learner Reviews & Feedback for Probabilistic Graphical Models 1: Representation by Stanford University

1,216 ratings
265 reviews

About the Course

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....

Top reviews


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!!


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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126 - 150 of 258 Reviews for Probabilistic Graphical Models 1: Representation

By llv23

Jul 19, 2017

Very good and excellent course and assignment

By Parag H S

Aug 14, 2019

Learn the basic things in probability theory

By Jonathan H

Nov 25, 2017

This course is hard and very interesting!

By Shengliang

May 29, 2017

excellent explanations! Thanks professor!

By Alexander K

May 16, 2017

Thank you for all. This is gift for us.

By Chahat C

May 04, 2019

lectures not good(i mean not detailed)

By Harshdeep S

Jul 19, 2019

Excellent blend of maths & intuition.


Mar 07, 2020

Very good explanation on the subject

By Jui-wen L

Jun 21, 2019

Easy to follow and very informative.

By Miriam F

Aug 27, 2017

Very nice and well prepared course!

By Gary H

Mar 28, 2018

Great instructor and information.

By George S

Jun 18, 2017

Excellent material presentation

By 郭玮

Apr 26, 2019

Really nice course, thank you!

By hyesung J

Oct 10, 2019

So difficult. But interesting

By Jinsun P

Jan 17, 2017

Really Helpful for Studying!

By Shengding H

Mar 10, 2019

A very nice-designed course

By Marno B

Feb 03, 2019

Absolutely love it!!!!


By Nguyễn L T Â

Feb 06, 2018

Thank you, the professor.

By hy395

Sep 13, 2017

Very clear and intuitive.

By 艾萨克

Nov 07, 2016

useful! A little diffcult

By Souvik C

Oct 26, 2016

Extremely helpful course

By Joris S

Feb 16, 2020

Well presented course!

By Jiew W

Apr 17, 2018

very good, practical.

By Wei C

Mar 06, 2018

good online coursera

By Nijesh

Jul 18, 2019

Thanks for offering