Learner Reviews & Feedback for Probabilistic Graphical Models 1: Representation by Stanford University
About the Course
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).
76 - 100 of 279 Reviews for Probabilistic Graphical Models 1: Representation
By Saikat M
•Aug 01, 2017
Not as rigorous as the book, but very good. However, Octave should not be be necessary and is a road block to completing assignments.
By Mohammd K D
•Apr 03, 2017
One of the best courses which i visited.
The explanation was so simple and there were many examples which were so helpful for me
By ALBERTO O A
•Oct 16, 2018
Really well structured course. The contents are complemented with the book. It is a time consuming course. Totally enjoyed!
By Mike P
•Jul 30, 2019
An excellent course, Daphne is one of the top people to be teaching this topic and does an excellent job in presentation.
By Matt M
•Oct 22, 2016
Very interesting and challenging course. Now hoping to apply some of the techniques to my Data Science work.
By Anton K
•May 07, 2018
This was my first experience with Coursera! Thanks prof. Daphne Koller for this course and Coursera at all.
By Kelvin L
•Aug 11, 2017
I guess this is probably the most challenging one in the Coursera. Really Hard but really rewarding course!
By 杨涛
•Mar 27, 2019
I think this course is quite useful for my own research, thanks Cousera for providing such a great course.
By HARDIAN L
•Jun 23, 2018
Even though this is the most difficult course I have ever taken in Coursera, I really enjoyed the process.
By Satish P
•Jul 13, 2020
A fantastic course and quite insightful. Require a strong grounding in probability theory to complete it.
By Johannes C
•Apr 19, 2020
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 Onur 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 Alfred D
•Jul 02, 2020
Was a little difficult in the middle but the last section summary just refreshed all of it
By Stephen F
•Feb 26, 2017
This is a course for those interested in advancing probabilistic modeling and computation.
By Una S
•Jul 24, 2020
Amazing!!! Loved how Daphne explained really complex materials and made them really easy!
By liang c
•Nov 15, 2016
Great course. and it is really a good chance to study it well under Koller's instruction.
By AlexanderV
•Mar 09, 2020
Great course, except that the programming assignments are in Matlab rather than Python
By Ning L
•Oct 18, 2016
This is a very good course for the foundation knowledge for AI related technologies.
