I really loved the dense collection of relevant information, this course is a great introduction to computer vision-related algorithms.

Unfortunately the lecture videos are poorly edited and subtitles are inaccurate, however the slides are quite good and verbose enough to understand every topic.

Assignments are quite good, however formula derivation explanations could be better.

It's a nice introduction, but a lot of details are not well explained. A lot of typos in homework, epecially in Equations. This is very bad for UX. And few of mentors are maintaining this course. If you ask questions in forum, you can not get repsonse quickly. I suggest that mentors should spend some time to correct typos and upload some supplement materials.

The content in the course and the expanse of knowledge covered is excellent. I would suggest that course be more organized in terms of terminology and usage of symbols. It does take time, but using different notations while explaining the same concepts causes confusion, at least during the learning phase. Overall a great course.

The content is undoubtedly valuable and instructive, but for some topics in week 3 & 4 the content isn't organized in a good order. Most importantly, no one answers my questions in the forum, and I felt helpless when the lecturer and slides fail to clearly explain some complex concepts.

The course had a lot of useful content and a lot of information. Although the presentation was not that good. The slides could have used more of text, there were only formula and images. Week 4 exercise could have been given a little more clearly in the Exercise sheet.

There were a lot of error in the materials even after all those years. Also the instructor's English is hard to understand sometimes. In addition to that they do a lot of waving around with their hands instead of marking things directly in the pictures.

The course is excellent is computer vision! The only problem it is not didactic at all, so if you don't are familiar with this content it will be very hard (even impossible) to follow.

Lectures sometimes scattered and hard to follow. More advanced visuals would be helpful for such a visual subject vs watching lecturers wave hands and point at things.

Topics are good and comprehensive, but videos are long and difficult to follow, with a lot of additional research necessary to truly understand the concepts.

The content was great and not so easy, you need good math-linear algebra background, bur for some reason I didn't enjoy the course :/

Too much theory and projects didn't related with real life problems.

Visual Perception (Computer Vision) < Perception

Too math oriented

Very though and little help in the discussion forum

The videos are so long in time and not very clear.

Lessons and videos are very long for one week.

Not at all formulated for bachelors

Professors very hard to understand, and videos don't have the best editing.

Also extremely math heavy, lectures being just math formulas, without a lot of real life applicability (ex. the assignments where you have to project a logo, or cube, when the program gives you the location corners in a file, which will NEVER happen when you use your camera; finding that position is a pre-requisite of the entire program)

This course should have been very good. Lots of detail. But the material is not presented very clearly. The Assignments are also not setup so that it is easy to fix your work. It either passes, or you get minimal feedback.

Worst course ever had on Coursera. Bad illustrations and cumbersome body languages just make the explanation of the concepts more confusing. Maybe the Andrew Ng's machine learning and deep learning specialization are just too good, so that it makes me feel this course is unacceptably bad.

I need help