Very clear and intuitive.

Great Course!!!!!

This was a fantastic introduction to PGM for a non-expert. It is well paced for an online course and the assignments provide enough depth to hone your knowledge and skills within the 5 week timeframe. Highly recommended.

The course was very fruitful. It is was not that easy of course, I think it is one of the most difficult courses on Coursera but it deserves to try it once, twice and as many as you can until you understand the idea behind the course. The exams and the honor assignments were so tricky and not that easy to solve. If you don't have a probabilistic background, I think first better for you to take a course in data analysis and probability.

Really Helpful for Studying!

Super useful if you want to understand any probability model.

Had a wonderful learning experience, Thank You Daphne Ma'am.

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

Quiz and Video Lecture content was good. Would have preferred different format for programming assignments. The 30 minute life time of programming assignment submission tokens was pretty inconvenient. Overall great course. Definitely more challenging than the Machine Learning course material.

very good, practical.

This course is quite interesting not that easy. It helps me understand Markov network. The questions within the video are very helpful. It helps me check out some essential concepts and details. What's more, I'm fascinated by the teacher's voice and her teaching style, though detailed reading is required off class to gain comprehensive understanding. This is the first time I take online course in courser, and it's fun. I think I'll keep on learning the rest 2 courses of this series.

This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.

Great course. The lectures are rather clear and the assignments are very insightful. It takes some time to complete, mostly if you are interested in doing the Honor programming assignments (and you really should be, because these are demanding but also very useful). Previous knowledge on basic probability theory and machine learning is highly recommended.

Awsome course for Information/Knowledge Engineering. Although not necessary to finish all the honor assignments, it is highly recommended to implement them. Not only for comprehension, but also practice. You can actually apply them on your career or research.

All Programming Assignments are challenging (Bayesian net, Markov net/CRF, and decision making), but very essential to help understand how PGM works. I definitely will enroll the second course in this specialization.

I really like how Dafna is teaching the course, very clear!

It will be nice if their could be a following course that will show new frameworks and code that implements PGMs. Like the courses of deep learning where Andrew Ng is focusing mostly on the practical side.

very nice

Sugerencia: Algunos de los ejemplos numéricos presentados en el curso podrían ir acompañados de alguna expresión matemática intermedia que facilite la comprensión de los mismos.

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

This course is hard and very interesting!

Very nice and well prepared course!

Brilliant content and charismatic lecturer!!!

Awesome!!!

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

Great course.