Learner Reviews & Feedback for Probabilistic Graphical Models 1: Representation by Stanford University

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.

I found well structured contend of these rare probabilistic methods (Actually this is the only reasonable course in this approach online)

Not as rigorous as the book, but very good. However, Octave should not be be necessary and is a road block to completing assignments.

One of the best courses which i visited.

The explanation was so simple and there were many examples which were so helpful for me

Really well structured course. The contents are complemented with the book. It is a time consuming course. Totally enjoyed!

An excellent course, Daphne is one of the top people to be teaching this topic and does an excellent job in presentation.

one of the best course I have ever followed. by all means it gave thorough understanding of every topic the introduced.

Very interesting and challenging course. Now hoping to apply some of the techniques to my Data Science work.

Great course. Lectures gives us good intuition on definitions and results. Programming assignments are fun.

This was my first experience with Coursera! Thanks prof. Daphne Koller for this course and Coursera at all.

I guess this is probably the most challenging one in the Coursera. Really Hard but really rewarding course!

I think this course is quite useful for my own research, thanks Cousera for providing such a great course.

Even though this is the most difficult course I have ever taken in Coursera, I really enjoyed the process.

A fantastic course and quite insightful. Require a strong grounding in probability theory to complete it.

necessary and vast toolset for every scientist, data scientist or AI enthusiast. Very clearly explained.

Great course. Interesting concepts to learn, but some of them are too quickly and poorly explained.

Awesome material. Could not get this experience by learning the subject ourselves using a textbook.

Some more exam questions and variation, including explanations when failing, would be very useful.

Great course. Recommended to everyone who have interest on bayesian networks and markov models.

Great course, looking forward for the following parts. Took it straight after Andrew Ng's one.

Very informative. The only drawback is lack of rigorous proof and clear definition summaries.

Brilliant. Optional Honours content was more challenging than I expected, but in a good way.

Awesome course! I feel like bayesian method is also very useful for inference in daily life.

Was a little difficult in the middle but the last section summary just refreshed all of it

This is a course for those interested in advancing probabilistic modeling and computation.