學生對 斯坦福大学 提供的 Probabilistic Graphical Models 1: Representation 的評價和反饋
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ST
2017年7月12日
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
2017年10月22日
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 個評論（共 304 個）
創建者 Johannes C
•2020年4月19日
necessary and vast toolset for every scientist, data scientist or AI enthusiast. Very clearly explained.
創建者 Alexandru I
•2018年11月25日
Great course. Interesting concepts to learn, but some of them are too quickly and poorly explained.
創建者 Rajmadhan E
•2017年8月7日
Awesome material. Could not get this experience by learning the subject ourselves using a textbook.
創建者 Lucian
•2017年1月15日
Some more exam questions and variation, including explanations when failing, would be very useful.
創建者 Onur B
•2018年11月13日
Great course. Recommended to everyone who have interest on bayesian networks and markov models.
創建者 Elvis S
•2016年10月28日
Great course, looking forward for the following parts. Took it straight after Andrew Ng's one.
創建者 Youwei Z
•2018年5月19日
Very informative. The only drawback is lack of rigorous proof and clear definition summaries.
創建者 Umais Z
•2018年8月23日
Brilliant. Optional Honours content was more challenging than I expected, but in a good way.
創建者 Hao G
•2016年11月1日
Awesome course! I feel like bayesian method is also very useful for inference in daily life.
創建者 Alfred D
•2020年7月2日
Was a little difficult in the middle but the last section summary just refreshed all of it
創建者 Stephen F
•2017年2月26日
This is a course for those interested in advancing probabilistic modeling and computation.
創建者 Una S
•2020年7月24日
Amazing!!! Loved how Daphne explained really complex materials and made them really easy!
創建者 liang c
•2016年11月15日
Great course. and it is really a good chance to study it well under Koller's instruction.
創建者 AlexanderV
•2020年3月9日
Great course, except that the programming assignments are in Matlab rather than Python
創建者 Ning L
•2016年10月17日
This is a very good course for the foundation knowledge for AI related technologies.
創建者 Hong F
•2020年6月21日
Hope there are explanations of the hard questions (marked by *) in the final exam.
創建者 Abhishek K
•2016年11月6日
Difficult yet very good to understand even after knowing about ML for a long time.
創建者 chen h
•2018年1月20日
The exercise is a little difficult. Need to revise several times to fully digest.
創建者 Isaac A
•2017年3月23日
A great introduction to Bayesian and Markov networks. Challenging but rewarding.
創建者 庭緯 任
•2017年1月10日
perfect lesson!! Although the course is hard, the professor teaches very well!!
創建者 Alejandro D P
•2018年6月29日
This and its sequels, the most interesting Coursera courses I've taken so far.
創建者 Naveen M N S
•2016年12月13日
Basic course, but has few nuances. Very well instructed by Prof Daphne Koller.
創建者 Amritesh T
•2016年11月25日
highly recommended if you wanna learn the basics of ML before getting into it.
創建者 Pouya E
•2019年10月13日
Well-structured content, engaging programming assignments in honors track.
創建者 David C
•2016年11月1日
If you are interested in graphical models, you should take this course.
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