Best CS-related course on coursera yet

Excellent content, quite challenging.

Easy way to approach the Probability

Very complete course. Recommended!

Very tough but very interesting

the jupyter file is outdated

Great course, quite hard.

Amazing contents

Awesome course !

Great course!!!!

Tough but a must

Very helpful!

gfhgngfbfv

Excellent!

very good

Succinct!

Cool!!

.

The course was great, I've learned a lot about Bayesian perspective on Machine Learning. The level was satisfying, tasks and quizzes were demanding. It has been very interesting material to learn about.

I would give 5 stars, but eventually gave 4 because it had two drawbacks. First is, assignments are written in tensorflow v1, and occasionally there were issues with compatibility in some libraries. I don't know when the codes were last time refreshed, but unfortunately open source technologies tend to become deprecated very quickly, and the time has already affected course materials. Secondly, some of the most important derivations were made on blackboard, and are not included in downloadable slides. I would really like to keep them in some files, but the're not available.

Apart from these minor drawbacks, it's still a great course and definitely worth learning from.

This is a great course for some of the advance topics in Baysian ML. The course starts off great and provides great explanation of the basic topics such as Conjugate, EM algorithm, etc. The related HW are also intelligently designed and fun to solve. But, as it reaches the weeks 5 and 6, things starts to fall apart and the materials are not presented and explained in the best possible way. I think the instructors try to teach many topics which requires a little bit of patience in a short amount of time. Overall, I believe its a course worthy of try, certainly provides great exposure to some of the advance topics but requires further follow ups and studies to completely digest all the materials.

Great course, great material, though difficult to follow a non native English speaker being non-english myself. Though the instructors know what they are talking about, they don't tell it in their own words but rather seem to have practiced their text.

Another important point is that it took me a lot of time to follow (pre)calculus and probability theory courses, to be able to understand this course. The course was a nice motivation to do that. I'm glad I did, because now I can understand and use VAE's and bayesian optimization (and some other useful stuff)

I can not recommend this course highly enough. Unfortunately I can't give it 5 stars since some of the computer assignments were outdated with respect to the tools they utilize (e.g. arguments in the assignments not existing anymore). Still, let that not discourage you. If you ever mentally disconnect when people start talking about Gibbs sampling, mean field approximations, intractable variational lower bounds, or other big fancy words, this is definitely the course for you. You'll discover that all these things are actually quite straightforward.

Fantastic course! Very comprehensive introduction to Bayesian analysis. There's though room for improvement from what I have experienced. One suggestion I have is to provide transcript written and checked by the lecturers and not some auto generated script! This would help us a lot better to understand the video and won't mislead us, which at times the transcript really does!!

Excellent, detailed content for people wanting to understand variational methods for machine learning. Fairly high degree of math and statistics required as a prerequisite, as well as moderate ability as a Python programmer. Does not get 5 stars because some of the assignments had confusing instructions, and availability of instructors and others to asnwer questions was poor.

Hard material, but very well explained. The peer-review exercises are interesting as well, but if the reviewer does not understand the material, I wonder how useful they are. Open research question I guess (i.e. how to make sure the student reviewer understands what he is reviewing-are there any baseline reviews established a student should go through first?)!