Learner Reviews & Feedback for Probabilistic Graphical Models 1: Representation by Stanford University
About the Course
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!!
CM
Oct 22, 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).
201 - 225 of 314 Reviews for Probabilistic Graphical Models 1: Representation
By David D
•May 30, 2017
Mind blowing!
By Yang P
•Apr 26, 2017
Great course.
By Nairouz M
•Feb 13, 2017
Very helpful.
By brotherzhao
•Feb 15, 2020
nice course!
By Utkarsh A
•Dec 30, 2018
maza aa gaya
By Musalula S
•Aug 2, 2018
Great course
By Yuri F
•May 15, 2017
great course
By 赵紫川
•Nov 27, 2016
Nice course.
By Pedro R
•Nov 9, 2016
great course
By Frank
•Dec 14, 2017
老师太天马行空了。。。
By HOLLY W
•May 24, 2019
课程特别好，资料丰富
By Siyeong L
•Jan 21, 2017
Awesome!!!
By Alireza N
•Jan 12, 2017
Excellent!
By dingjingtao
•Jan 7, 2017
excellent!
By Phan T B
•Dec 2, 2016
very good!
By Jax
•Jan 8, 2017
very nice
By Jose A A S
•Nov 25, 2016
Wonderful
By Mohammed O E A
•Oct 18, 2016
Fantastic
By zhou
•Oct 13, 2016
very good
By 张浩悦
•Nov 22, 2018
funny！！
By Alexander A S G
•Feb 9, 2017
Thanks
By oilover
•Dec 2, 2016
老师很棒！！
By 刘仕琪
•Oct 30, 2016
不错的一门课
By Accenture X
•Oct 12, 2016
Great
By Ludovic P
•Oct 29, 2017
I wish I could give 4 and a half star to this course.
On the positive side : there is a lot of value in this course. Professor Koller succeeds in introducing us to PGM representations in a few weeks. IMHO, one should really do all the exercices "for a mention". Without them, this course lacks "hands on" sessions, and is much less interesting. Most programming exercises are great, and the companion quiz are really a plus.
When I followed Professor Ng programming exercises, I was both delighted and frustrated. Delighted because I learned a lot of things. Frustrated because it was sometimes really too easy.
This is not the case for most exercices there. I find them so well prepared, so crafted that I often learned a lot of my first wrong submissions of quiz of programming exercices.
On the negative side : the quality of the sound recordings is sometimes not really good. That is especially true in the first videos. That should not stop you from following this great course ! Some programming exercices were a bit frustrating because their difficulty is more in knowing octave tips and tricks than in PGM. In addition, and this is more embarassing, some exercices do not work, like in Markov Network for OCR https://www.coursera.org/learn/probabilistic-graphical-models/programming/dZmtj/markov-networks-for-ocr I had, as other students, to disable some features and to blindly submit my ansmwers.
Also, some exercises were difficult for me because of very precise English. I guess it might be difficult for native speakers to handle that, but as this course seems to have an international audience, it would be great.
I feel that raising this great course from 4 stars to 5 stars would not require much efforts. Prepare better recordings of the few videos that have really bad sound. Correct those small bugs in exercises. Simplify some English wordings.
I, however, advise this course to all persons interested in this field. And I intend to follow the next course, on inference.