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

4.6
stars
1,435 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 proble...
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Top reviews

RG

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!!

AB

Aug 30, 2018

Excellent course, the effort of the instructor is well reflected in the content and the exercices. A must for every serious student on (decision theory or markov random fields tasks.

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

By Volodymyr D

Apr 11, 2020

Useful course on great subject, but poorly explained and supported. It was quite hard for me to get implicit ideas and Honors assignments. I ended up skipping Honors assignments since they're explained really really poorly and most of the time I spent trying to figure out what I'm required to do. Forums are inactive and no mentors reply to the posts. I don't recommend taking this course if you don't have someone to guide and help you.

By Sami J

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

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

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

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

Dec 13, 2016

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

By Jonathan K

Jan 26, 2018

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

By Michel S

Jul 14, 2018

Good course, but the material really needs a refresh!

By Robert M

Feb 6, 2018

Started off well. Finished poorly

By Aswin T

Sep 10, 2020

Very rigid questions, very theoretical. Very poor instructor support. Content needs to be improved. Very disconnected approach.

By Deleted A

Jun 11, 2020

very shallow explanation of important concepts

By Shan-Jyun W

Jun 24, 2017

Lectures are awful.

By Belal M

Sep 8, 2017

A very dry course.

By Javier G

Aug 4, 2020

Muy malo