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.

an excellent course that lay my foundation of bayesian network

Good way to learn Probabilistic Graphical Models in practical

Challenging! Regret not doing the coding assignment for honors

Great course loved the ongoing feedback when doing the quizes.

a bit too hard if you don't have enough probability knowledge

Best course anywhere on this topic. Plus Daphne is the best !

Super useful if you want to understand any probability model.