学生对 Stanford University 提供的 Probabilistic Graphical Models 1: Representation 的评价和反馈
课程概述
热门审阅
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.
201 - Probabilistic Graphical Models 1: Representation 的 225 个评论（共 314 个）
创建者 David D
•May 29, 2017
Mind blowing!
创建者 Yang P
•Apr 26, 2017
Great course.
创建者 Nairouz M
•Feb 13, 2017
Very helpful.
创建者 brotherzhao
•Feb 15, 2020
nice course!
创建者 Utkarsh A
•Dec 30, 2018
maza aa gaya
创建者 Musalula S
•Aug 2, 2018
Great course
创建者 Yuri F
•May 15, 2017
great course
创建者 赵紫川
•Nov 26, 2016
Nice course.
创建者 Pedro R
•Nov 9, 2016
great course
创建者 Frank
•Dec 14, 2017
老师太天马行空了。。。
创建者 HOLLY W
•May 24, 2019
课程特别好，资料丰富
创建者 Siyeong L
•Jan 21, 2017
Awesome!!!
创建者 Alireza N
•Jan 12, 2017
Excellent!
创建者 dingjingtao
•Jan 7, 2017
excellent!
创建者 Phan T B
•Dec 2, 2016
very good!
创建者 Jax
•Jan 8, 2017
very nice
创建者 Jose A A S
•Nov 25, 2016
Wonderful
创建者 Mohammed O E A
•Oct 17, 2016
Fantastic
创建者 zhou
•Oct 13, 2016
very good
创建者 张浩悦
•Nov 22, 2018
funny！！
创建者 Alexander A S G
•Feb 9, 2017
Thanks
创建者 oilover
•Dec 2, 2016
老师很棒！！
创建者 刘仕琪
•Oct 30, 2016
不错的一门课
创建者 Accenture X
•Oct 12, 2016
Great
创建者 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://hua.dididi.sbs/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.