学生对 斯坦福大学 提供的 Probabilistic Graphical Models 1: Representation 的评价和反馈
课程概述
热门审阅
ST
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).
101 - Probabilistic Graphical Models 1: Representation 的 125 个评论（共 304 个）
创建者 Johannes C
•Apr 19, 2020
necessary and vast toolset for every scientist, data scientist or AI enthusiast. Very clearly explained.
创建者 Alexandru I
•Nov 25, 2018
Great course. Interesting concepts to learn, but some of them are too quickly and poorly explained.
创建者 Rajmadhan E
•Aug 7, 2017
Awesome material. Could not get this experience by learning the subject ourselves using a textbook.
创建者 Lucian
•Jan 15, 2017
Some more exam questions and variation, including explanations when failing, would be very useful.
创建者 Onur B
•Nov 13, 2018
Great course. Recommended to everyone who have interest on bayesian networks and markov models.
创建者 Elvis S
•Oct 28, 2016
Great course, looking forward for the following parts. Took it straight after Andrew Ng's one.
创建者 Youwei Z
•May 19, 2018
Very informative. The only drawback is lack of rigorous proof and clear definition summaries.
创建者 Umais Z
•Aug 23, 2018
Brilliant. Optional Honours content was more challenging than I expected, but in a good way.
创建者 Hao G
•Nov 1, 2016
Awesome course! I feel like bayesian method is also very useful for inference in daily life.
创建者 Alfred D
•Jul 2, 2020
Was a little difficult in the middle but the last section summary just refreshed all of it
创建者 Stephen F
•Feb 26, 2017
This is a course for those interested in advancing probabilistic modeling and computation.
创建者 Una S
•Jul 24, 2020
Amazing!!! Loved how Daphne explained really complex materials and made them really easy!
创建者 liang c
•Nov 15, 2016
Great course. and it is really a good chance to study it well under Koller's instruction.
创建者 AlexanderV
•Mar 9, 2020
Great course, except that the programming assignments are in Matlab rather than Python
创建者 Ning L
•Oct 17, 2016
This is a very good course for the foundation knowledge for AI related technologies.
创建者 Hong F
•Jun 21, 2020
Hope there are explanations of the hard questions (marked by *) in the final exam.
创建者 Abhishek K
•Nov 6, 2016
Difficult yet very good to understand even after knowing about ML for a long time.
创建者 chen h
•Jan 20, 2018
The exercise is a little difficult. Need to revise several times to fully digest.
创建者 Isaac A
•Mar 23, 2017
A great introduction to Bayesian and Markov networks. Challenging but rewarding.
创建者 庭緯 任
•Jan 10, 2017
perfect lesson!! Although the course is hard, the professor teaches very well!!
创建者 Alejandro D P
•Jun 29, 2018
This and its sequels, the most interesting Coursera courses I've taken so far.
创建者 Naveen M N S
•Dec 13, 2016
Basic course, but has few nuances. Very well instructed by Prof Daphne Koller.
创建者 Amritesh T
•Nov 25, 2016
highly recommended if you wanna learn the basics of ML before getting into it.
创建者 Pouya E
•Oct 13, 2019
Well-structured content, engaging programming assignments in honors track.
创建者 David C
•Nov 1, 2016
If you are interested in graphical models, you should take this course.
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