Learner Reviews & Feedback for Probabilistic Graphical Models 1: Representation by Stanford University
About the Course
Top reviews
SC
Nov 4, 2016
The course is great with plenty of knowledge. A little defect is about description about assignment. As the forum discussed, several quizzes may confusing.
CC
Mar 24, 2020
really great course! very clear and logical structure. I completed a graphical models course as part of my master's degree, and this really helped to consolidate it
101 - 125 of 314 Reviews for Probabilistic Graphical Models 1: Representation
By Singhi K
•Aug 1, 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 Karam D
•Apr 3, 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 Pathirage D
•May 29, 2021
one of the best course I have ever followed. by all means it gave thorough understanding of every topic the introduced.
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 Samuel B
•Mar 13, 2021
Great course. Lectures gives us good intuition on definitions and results. Programming assignments are fun.
By Anton K
•May 7, 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 12, 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 7, 2017
Awesome material. Could not get this experience by learning the subject ourselves using a textbook.
By Lucian
•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 28, 2016
Great course, looking forward for the following parts. Took it straight after Andrew Ng's one.
By Youwei Z
•May 19, 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 1, 2016
Awesome course! I feel like bayesian method is also very useful for inference in daily life.
By Alfred D
•Jul 2, 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.