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.

Amazing!!! Loved how Daphne explained really complex materials and made them really easy!

Great course. and it is really a good chance to study it well under Koller's instruction.

Great course, except that the programming assignments are in Matlab rather than Python

This is a very good course for the foundation knowledge for AI related technologies.

Hope there are explanations of the hard questions (marked by *) in the final exam.

Difficult yet very good to understand even after knowing about ML for a long time.

The exercise is a little difficult. Need to revise several times to fully digest.

A great introduction to Bayesian and Markov networks. Challenging but rewarding.

perfect lesson!! Although the course is hard, the professor teaches very well!!

This and its sequels, the most interesting Coursera courses I've taken so far.

Basic course, but has few nuances. Very well instructed by Prof Daphne Koller.

highly recommended if you wanna learn the basics of ML before getting into it.

Well-structured content, engaging programming assignments in honors track.

If you are interested in graphical models, you should take this course.

Professor Koller does an amazing job, I fully recommend this course

Awesome Course. I got to learn a lot of useful concepts. Thank You.

Excellent course! This course helps me so much studying about PGM!

A great course! The provided training clarifies all key concepts

Great course. Thanks Daphne Koller, this is really motivating :)

A great course, a must for those in the machine learning domain.

Good way to learn Probabilistic Graphical Models in practical