Avis et commentaires pour d'étudiants pour Probabilistic Graphical Models 1: Representation par Université de Stanford
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Meilleurs avis
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!!
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).
76 - 100 sur 281 Avis pour Probabilistic Graphical Models 1: Representation
par Isaiah O M
•I found well structured contend of these rare probabilistic methods (Actually this is the only reasonable course in this approach online)
par Saikat M
•Not as rigorous as the book, but very good. However, Octave should not be be necessary and is a road block to completing assignments.
par Mohammd K D
•One of the best courses which i visited.
The explanation was so simple and there were many examples which were so helpful for me
par ALBERTO O A
•Really well structured course. The contents are complemented with the book. It is a time consuming course. Totally enjoyed!
par Mike P
•An excellent course, Daphne is one of the top people to be teaching this topic and does an excellent job in presentation.
par Matt M
•Very interesting and challenging course. Now hoping to apply some of the techniques to my Data Science work.
par Anton K
•This was my first experience with Coursera! Thanks prof. Daphne Koller for this course and Coursera at all.
par Kelvin L
•I guess this is probably the most challenging one in the Coursera. Really Hard but really rewarding course!
par 杨涛
•I think this course is quite useful for my own research, thanks Cousera for providing such a great course.
par HARDIAN L
•Even though this is the most difficult course I have ever taken in Coursera, I really enjoyed the process.
par satish p
•A fantastic course and quite insightful. Require a strong grounding in probability theory to complete it.
par Johannes C
•necessary and vast toolset for every scientist, data scientist or AI enthusiast. Very clearly explained.
par Alexandru I
•Great course. Interesting concepts to learn, but some of them are too quickly and poorly explained.
par Rajmadhan E
•Awesome material. Could not get this experience by learning the subject ourselves using a textbook.
par Lucian B
•Some more exam questions and variation, including explanations when failing, would be very useful.
par Onur B
•Great course. Recommended to everyone who have interest on bayesian networks and markov models.
par Elvis S
•Great course, looking forward for the following parts. Took it straight after Andrew Ng's one.
par Youwei Z
•Very informative. The only drawback is lack of rigorous proof and clear definition summaries.
par Umais Z
•Brilliant. Optional Honours content was more challenging than I expected, but in a good way.
par Hao G
•Awesome course! I feel like bayesian method is also very useful for inference in daily life.
par Alfred D
•Was a little difficult in the middle but the last section summary just refreshed all of it
par Stephen F
•This is a course for those interested in advancing probabilistic modeling and computation.
par Una S
•Amazing!!! Loved how Daphne explained really complex materials and made them really easy!
par liang c
•Great course. and it is really a good chance to study it well under Koller's instruction.
par AlexanderV
•Great course, except that the programming assignments are in Matlab rather than Python
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