I subscribe to Coursera so I can take as many courses as I like for a monthly fee. There are a lot of excellent courses on Coursera but this isn't one of them. I would be really angry if I had paid specifically for this course. There are issues with the practice quizzes that were pointed out in the discussion forum months ago for which there is still no reply. Staff should at least glance at the forum to see if there are any problems with the course material. The lectures cover the simple ideas, but the harder material is outsourced mostly to http://mathonline.wikidot.com/ and sundry pdf documents. Some of the reading material is a lot more advanced than the course itself.

This Course content is very good and has good real-time examples. However, the Instructor should add a few videos on SVD, Maximum dilation, and Shrinkage and Direction of Maximum Dilation.

Good set of sections and examples, important topics are considered. Peculiar lecture style (unusual for Coursera), but it's ok. It would be a good idea to add a videos to the last module.

I think it was a good practice. Thanks!

Thank you very much

The course topics are very important for Data Science and Machine Learning. However, except for a few resource materials in PDF document, the course does not meet the expectations and standards. The sessions covered seem to be incomplete, the type of questions asked in assignments are totally different as compared to what is being covered. Moreover, the discussion forum is totally non responsive and thus, if one has to complete this course and learn something out of it, then external resources need to fetched and comprehended. Hoped for a much better experience that what I had.

The course is taught interestingly for the first 3 weeks. The course material for week 4 becomes somhow unclear. Then, for the last week, only some readings are suggested, which are not really helpful or relevant to the upcoming questions.

All readings are well chosen , and actually helped me understand.

The video quality needs to be improved. Better explanations to some sections could help along with explanations to why answers were right or wrong in Assignments

The python content in this course is almost zero. The only thing I learn useful is the section about "singualr value decomposition" (the only reason why I still give it a 2-star review). However there's no lecture about the topic but two papers to read through. The instructor only contributes to easy matrix stuff in first three weeks and convinently skip the harder content in week 5. The video is not instructive enough. Compare to other courses from Coursera, this course is poor in quality and preparation.

Mentor didn´t give the last class and the "extra" material wasn´t helpful at all.

Lack of explanation especially for the last two weeks

This course is obviously home-made. Why the University of Minnesota permitted it to be uploaded to the Coursera platform is something I cannot fathom; it is by far the shabbiest course I have ever reviewed at Coursera. All topics presented are covered much more effectively in other courses. For example, SVD is covered by Nathan Kutz on his youtube AMATH channel, with applications. The economics of MOOCs is still a bit mysterious to me, but both Coursera and UMinn must financially benefit in at least some small way by having this content here, but why rely on Daniel Boley when we have Gilbert Strang, Nathan Kutz, Grant Sanderson, and many more teachers who actually care about imparting knowledge. If you look at his CV, Boley is research-oriented and obviously no longer cares a whit for teaching at this level. What we get is a review and a rather spotty one, at that, aimed at on-campus students in some other area at UMinn itself who have already mastered the topic.

This course is really bad. There are a lot of mistakes in reading material and exercises. Videos are poor and not clear. Teaching stuff doesn't respond. The first two weeks were fine, but others are not. The last week doesn't have any videos at all. The reading material is from different sites and books and has no structure.

I honestly think this is the worst course I ever took in Coursera. The classes are short and boring, and the Week 5 section didn't provide any relevant material for the assignments, not even a single video.

Please do not ever take this course, there's like five videos of 4 minutes each and they're kinda old...

no ofense, but i think there are way to better courses out there

Lecture and reading materials and very brief and don't cover all the topics on the assignments & quizzes. There are no lectures on SVD.

Video supplementary material is quite poor

No explanations.

bad taste!!

The MOOC is outstanding, and it covers the most important chapters of Linear Algebra for someone who likes to master knowledge in Econometrics, Machine Learning, Image Processing, and classical topics of Operations Research and Optimization. The course is not introductory, which means if you know beforehand Python programming, it helps to understand in depth the material. For the mathematical concepts, it also needs a good background in Linear Algebra and Matrices and general Analytic Geometry, as well as Norms and Inner Products. The material is divided into a sequence of different mathematics courses in short video lectures as a quick revision. The course gives an applied review of specific topics but with challenging exercises. The only drawback is the poor video material, especially in the last week, there is no video lecture and the coding is elementary. Instead, I have studied what I found on the internet in other sources, and I am very satisfied about new things in Machine Learning methods in Matrix Algebra. The course material is just a guideline and it can be found with a similar title: Engineering Mathematics of Information Management or Mechatronics or Economics.

Thanks to my professor Daniel for your great effort and interesting lecturers. There's interesting things in your course which will help us in our future.

Thanks to University of Minnesota to organized such courses for your students and teachers.