Comentários e feedback de alunos de Probabilistic Graphical Models 1: Representation da instituição Universidade de Stanford
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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 300 Avaliações para o Probabilistic Graphical Models 1: Representation
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.
por Camilo G
•4 de fev de 2020
Professor Koller does an amazing job, I fully recommend this course
por PRABAL B D
•1 de set de 2018
Awesome Course. I got to learn a lot of useful concepts. Thank You.
por Pham T T
•13 de dez de 2019
Excellent course! This course helps me so much studying about PGM!
por Lik M C
•12 de jan de 2019
A great course! The provided training clarifies all key concepts
por Sivaramakrishnan V
•6 de jan de 2017
Great course. Thanks Daphne Koller, this is really motivating :)
por Arjun V
•3 de dez de 2016
A great course, a must for those in the machine learning domain.
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