This course is taught at a really low level. Exercises are in spreadsheets which are more or less useless for practicing scale data applications. Spreadsheets contain information that makes importation into numerical processing software such as Pandas in Python or dplyr in R needlessly difficult and assumes the user can't even apply the distance formula.

Videos contain useful information but require wading through a lot of garbage at a slow pace, not useful for practitioners.

Assignments are poorly worded and some terminology is used questionably or flexibly (see the word "normalization"). Some assignments are so poorly done that there is an ongoing debate on the forums as to whether the autograder is messed up or the assignment instructions are messed up.

The "honors" track programming assignments use some piece of software with questionable generalizability. If I ever see lens kit in my own data work environment I will come back an edit my review but I find it unlikely. Furthermore, Java is not commonly used for data science or machine learning purposes making these assignments inaccessible to many users. Personally, I write in Java but I didn't find it fulfilling to waste my time playing "fill in the blanks" or "guess the library function" which is overall uninstructive.

Quiz assignments show true indications of the poor level of instruction. Recitation of pieces of information buried in 30 minutes videos that can be condensed into 5 are some of the finest examples of bad teaching. Regurgitating information found in required readings shows no level of comprehension of course material and is a severe disservice to students.

I will hope for better general coverage of recommender systems in the future in another course. Ideally using something applicable like Python, Scala (Spark), or even R.

instructions for assignments are not clear! Lectures are good, but its practically impossible to get the certificate.

(+) The course material is good with real world examples and interviews with different people.

(+) Interesting material

(-) The assignments had mistakes.

(-) There is no example provided for practice before jumping into assignments.

There is good information in this course but there are so many problems in this course. There are major errors in the assignments and I was only about the get the right answers by reading the discussions on the message board. There are coding exercises but they expect you to write them in Java rather than a language used by data scientists such as Python or R. It is a good thing the made them optional.

The course is less helpful than the others in the specialty. The lecture should include an example to help clarify the understanding necessary for Quiz Part II and Part IV. The instructors didn't respond to the many questions in Week 4 forum and I was unable to complete the course.

Some exercises have wrong directions !!!

It would be better to provide other programming language such as python in honour assignment. And in the assignment should more emphasis on the algorithm not rely on too much others such as Lenskit.

Extremely informative course! It would be great if the assignments are created on python or R in the next season's offering. Thanks for the knowledge!

There is an error in the assignment week 4 : the spreadsheet normalize by user instead of by item

Basic concepts were introduced really well and the assignments were helpful in applying the same, but a major portion of the course was covered with interviews with experts. The interviews were informative but didn't help much in getting hands-on experience about the stuff they were talking about. It would be a whole lot better if there were some mathematical exercises involved related to the advanced concepts introduced by the experts.

The course material is good, but the course itself is merely okay due to some problems with the assignments that have gone unaddressed for years. The Item-Item filtering assignment solution does not match the formula given in the lectures, and the honors assignments use an outdated version of the code (at one point recommending a package that has been deprecated). Really needs some attention to fix bugs and update the software.

good introduction to topics and algorithms but very little help provided for the assignment in clarifying doubts in forums and unclear explanations were given for assignments. also not providing option to use any other programming language like python or r to do programming assignment is a big miss. would still recommend this course to get started from basics about reco sys.

The course itself is interesting, but some of the programming assignments are horribly confusing, what makes you waste your time trying to decipher what the professor really meant. Spreadsheet assignment on Week 3 is the main reason I rate this course so low, and a lot of people on discussion forums agree with me on assignment quality

Making honours programming exercise in Java is a mistake. Pls consider Python in the future. Assignment for week 4 uses formula differs from the course: wasted many hours that don't benefit learning.

The course is pretty good, but the spreadsheet assignments are brutal: they are confusing, too tedious and don't have enough information to debug.

THe item based assignment, parts II and IV didn't give enough guidance. Otherwise a decent course.

Week 4 assignments can do with a bit more clarity.

Assignments are not explained so well on this one

the hands-on quiz is not well prepared.

Lessons need more examples.

The content of the course is extremely useful, however assignments need review as the exercises results have mistakes and they are not explained very well (missing step by step guidance).

Not clear examples in my opinion, and there was same complain made from several user and I never saw a reply and nothing was changed

Unfortunately, the content of the course is poor. Too many interviews and some of them are pointless.

Extremely subpar.

Great course, nice theory and interesting exercise with the sheets and making actual Java programs to implement the algorithms. I would love to see some more in-depth probability theory, and considerations about when the algorithms deviate from the theory, or connections to other theories, but I suppose the course is more accessible and interesting like this. The interviews are probably my favorite part!