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