ST
Jul 12, 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
Oct 22, 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).
创建者 John P
•Jun 16, 2022
A comprehensive introduction and review of how to represent joint probability distributions as graphs and basic causal reasoning and decision making.
创建者 Vivek G
•Apr 27, 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
创建者 Sureerat R
•Mar 2, 2018
This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.
创建者 Angel G G
•Dec 12, 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.
创建者 Ayush T
•Aug 23, 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.
创建者 Valeriy Z
•Nov 13, 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.
创建者 Mulang' O
•Mar 31, 2019
I found well structured contend of these rare probabilistic methods (Actually this is the only reasonable course in this approach online)
创建者 Singhi K
•Aug 1, 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.
创建者 Karam D
•Apr 3, 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
创建者 ALBERTO O A
•Oct 16, 2018
Really well structured course. The contents are complemented with the book. It is a time consuming course. Totally enjoyed!
创建者 Mike P
•Jul 30, 2019
An excellent course, Daphne is one of the top people to be teaching this topic and does an excellent job in presentation.
创建者 Pathirage D
•May 29, 2021
one of the best course I have ever followed. by all means it gave thorough understanding of every topic the introduced.
创建者 Matt M
•Oct 22, 2016
Very interesting and challenging course. Now hoping to apply some of the techniques to my Data Science work.
创建者 Samuel d S B
•Mar 13, 2021
Great course. Lectures gives us good intuition on definitions and results. Programming assignments are fun.
创建者 Anton K
•May 7, 2018
This was my first experience with Coursera! Thanks prof. Daphne Koller for this course and Coursera at all.
创建者 Kelvin L
•Aug 11, 2017
I guess this is probably the most challenging one in the Coursera. Really Hard but really rewarding course!
创建者 杨涛
•Mar 27, 2019
I think this course is quite useful for my own research, thanks Cousera for providing such a great course.
创建者 HARDIAN L
•Jun 23, 2018
Even though this is the most difficult course I have ever taken in Coursera, I really enjoyed the process.
创建者 Satish P
•Jul 12, 2020
A fantastic course and quite insightful. Require a strong grounding in probability theory to complete it.
创建者 Johannes C
•Apr 19, 2020
necessary and vast toolset for every scientist, data scientist or AI enthusiast. Very clearly explained.
创建者 Alexandru I
•Nov 25, 2018
Great course. Interesting concepts to learn, but some of them are too quickly and poorly explained.
创建者 Rajmadhan E
•Aug 7, 2017
Awesome material. Could not get this experience by learning the subject ourselves using a textbook.
创建者 Lucian
•Jan 15, 2017
Some more exam questions and variation, including explanations when failing, would be very useful.
创建者 Onur B
•Nov 13, 2018
Great course. Recommended to everyone who have interest on bayesian networks and markov models.
创建者 Elvis S
•Oct 28, 2016
Great course, looking forward for the following parts. Took it straight after Andrew Ng's one.