Name: Probabilistic Graphical Models 1: Representation
Brand: Stanford University
Availability: OnlineOnly
Rating: 4.6 (314 reviews)

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

4.6

stars

1,441 ratings

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

Dec 7, 2016

Very well designed. There were areas here I struggled with the technical details and had to read up a lot to understand. The assignments are very well designed.

Jul 12, 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!!

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301 - 314 of 314 Reviews for Probabilistic Graphical Models 1: Representation

By Sami J

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Apr 22, 2020

Material is interesting but needs updating. Programming assignments have been marked as "Honors Assignments", which is a thinly veiled attempt to shirk responsibility for fixing bugs and providing student support. Quiz questions are vaguely worded. Overall the course is challenging, but only sometimes for the right reasons.

By Shen C

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Jul 14, 2020

this course is a very difficult one. takes a lot of time and effort. forum is really useful (i wouldn't have passed without it). that said, it is also because there is little help from the lecturer and instructors. would appreciate more help.

By Siavash R

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Aug 10, 2017

For me this was a difficult course not because of the material, but because of the teaching style. I don't think Dr. Koller is a very good teacher.

By Xingjian Z

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Nov 2, 2017

Fun topic. But the explanation of the mentor is somewhat vague and the material is sometimes outdated and misleading.

By Ujjval P

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Dec 13, 2016

Concepts covered in quiz and assignments are not covered well in the lecture videos, can be much better.

By Jonathan K

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Jan 26, 2018

Interesting and useful material, but I found the lecturer unengaging.

By Michel S

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Jul 14, 2018

Good course, but the material really needs a refresh!

By Robert M

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Feb 6, 2018

Started off well. Finished poorly

By tegh s

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Dec 29, 2025

I have learn a lot throughout the course. The content is quite niche. It is hard find good resources for the content else where.

That said there were a couple of issues.

- The submit_configuration file needs to be modified due to programming errors for MATLAB

- Programming assignments should be given in Python not MATLAB

- To make the assignments more clear it would be good if you had video on how to test the code students submit before submitting. Week 3 is quite confusing.