Learner Reviews & Feedback for Probabilistic Graphical Models 1: Representation by Stanford University
About the Course
Top reviews
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!!
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).
76 - 100 of 252 Reviews for Probabilistic Graphical Models 1: Representation
By Johannes C
•Mar 08, 2018
necessary and vast toolset for every scientist, data scientist or AI enthusiast. Very clearly explained.
By Alexandru I
•Nov 25, 2018
Great course. Interesting concepts to learn, but some of them are too quickly and poorly explained.
By Rajmadhan E
•Aug 07, 2017
Awesome material. Could not get this experience by learning the subject ourselves using a textbook.
By Lucian B
•Jan 15, 2017
Some more exam questions and variation, including explanations when failing, would be very useful.
By BOnur b
•Nov 13, 2018
Great course. Recommended to everyone who have interest on bayesian networks and markov models.
By Elvis S
•Oct 29, 2016
Great course, looking forward for the following parts. Took it straight after Andrew Ng's one.
By Youwei Z
•May 20, 2018
Very informative. The only drawback is lack of rigorous proof and clear definition summaries.
By Umais Z
•Aug 23, 2018
Brilliant. Optional Honours content was more challenging than I expected, but in a good way.
By Hao G
•Nov 01, 2016
Awesome course! I feel like bayesian method is also very useful for inference in daily life.
By Stephen F
•Feb 26, 2017
This is a course for those interested in advancing probabilistic modeling and computation.
By liang c
•Nov 15, 2016
Great course. and it is really a good chance to study it well under Koller's instruction.
By Ning L
•Oct 18, 2016
This is a very good course for the foundation knowledge for AI related technologies.
By Abhishek K
•Nov 06, 2016
Difficult yet very good to understand even after knowing about ML for a long time.
By chen h
•Jan 21, 2018
The exercise is a little difficult. Need to revise several times to fully digest.
By Isaac A
•Mar 23, 2017
A great introduction to Bayesian and Markov networks. Challenging but rewarding.
By 庭緯 任
•Jan 10, 2017
perfect lesson!! Although the course is hard, the professor teaches very well!!
By Alejandro D P
•Jun 30, 2018
This and its sequels, the most interesting Coursera courses I've taken so far.
By Naveen M N S
•Dec 13, 2016
Basic course, but has few nuances. Very well instructed by Prof Daphne Koller.
By Amritesh T
•Nov 25, 2016
highly recommended if you wanna learn the basics of ML before getting into it.
By Pouya E
•Oct 13, 2019
Well-structured content, engaging programming assignments in honors track.
By David C
•Nov 01, 2016
If you are interested in graphical models, you should take this course.
By Camilo G
•Feb 05, 2020
Professor Koller does an amazing job, I fully recommend this course
By PRABAL B D
•Sep 01, 2018
Awesome Course. I got to learn a lot of useful concepts. Thank You.
By Pham T T
•Dec 13, 2019
Excellent course! This course helps me so much studying about PGM!
By Lik M C
•Jan 12, 2019
A great course! The provided training clarifies all key concepts
