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!!
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).
por John P•
16 de jun de 2022
A comprehensive introduction and review of how to represent joint probability distributions as graphs and basic causal reasoning and decision making.
por Vivek G•
27 de abr de 2019
Great course. some programming assignments are tough (not too nicely worded and automatic grader can be a bit annoying) but all in all, great course
por Sureerat R•
2 de mar de 2018
This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.
por Angel G G•
12 de dez de 2019
Great course, I miss some programming assignments (I didn't do the "honors"), but the quizzes are already good to test your general understanding.
por Ayush T•
23 de ago de 2019
This course is really good. It is well organized and taught in the best way which really helped me to implement similar ideas for my projects.
por Valeriy Z•
13 de nov de 2017
This course gives a solid basis for the understanding of PGMs. Don't take it too fast. It takes some time to get used to all the concepts.
por Mulang' O•
31 de mar de 2019
I found well structured contend of these rare probabilistic methods (Actually this is the only reasonable course in this approach online)
por Singhi K•
1 de ago de 2017
Not as rigorous as the book, but very good. However, Octave should not be be necessary and is a road block to completing assignments.
por Karam D•
3 de abr de 2017
One of the best courses which i visited.
The explanation was so simple and there were many examples which were so helpful for me
por ALBERTO O A•
16 de out de 2018
Really well structured course. The contents are complemented with the book. It is a time consuming course. Totally enjoyed!
por Mike P•
30 de jul de 2019
An excellent course, Daphne is one of the top people to be teaching this topic and does an excellent job in presentation.
por Pathirage D•
29 de mai de 2021
one of the best course I have ever followed. by all means it gave thorough understanding of every topic the introduced.
por Matt M•
22 de out de 2016
Very interesting and challenging course. Now hoping to apply some of the techniques to my Data Science work.
por Samuel B•
13 de mar de 2021
Great course. Lectures gives us good intuition on definitions and results. Programming assignments are fun.
por Anton K•
7 de mai de 2018
This was my first experience with Coursera! Thanks prof. Daphne Koller for this course and Coursera at all.
por Kelvin L•
11 de ago de 2017
I guess this is probably the most challenging one in the Coursera. Really Hard but really rewarding course!
27 de mar de 2019
I think this course is quite useful for my own research, thanks Cousera for providing such a great course.
por HARDIAN L•
23 de jun de 2018
Even though this is the most difficult course I have ever taken in Coursera, I really enjoyed the process.
por Satish P•
12 de jul de 2020
A fantastic course and quite insightful. Require a strong grounding in probability theory to complete it.
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.
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.