Michael D. Ekstrand
This capstone project course for the Recommender Systems Specialization brings together everything you’ve learned about recommender systems algorithms and evaluation into a comprehensive recommender analysis and design project. You will be given a case study to complete where you have to select and justify the design of a recommender system through analysis of recommender goals and algorithm performance. Learners in the honors track will focus on experimental evaluation of the algorithms against medium sized datasets. The standard track will include a mix of provided results and spreadsheet exploration. Both groups will produce a capstone report documenting the analysis, the selected solution, and the justification for that solution. The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world–renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
Instructor Details
Courses : 1
Specification: Recommender Systems Capstone
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4 reviews for Recommender Systems Capstone
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| Price | Free |
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| Provider | |
| Duration | 8 hours |
| Year | 2018 |
| Language | English |
| Certificate | Yes |
| Quizzes | Yes |
Oleg D –
It is the worst capstone project I have seen so far on Coursera peer evaluation is awfuly orgnaised, cirterias for it are very mechanical and it is not clear how evaluation criteria match the really interesting business case that was provided fir the capstone. The worst thing is that it is not even clear what to do with the task offered at the capstone, as it is clear that most of the methods described within the course would be not very efficietn with capstone use case, and techniques really suitable for it were covered just briefly during the course.
Nate D –
The workload was quite reasonable in the first four courses of the recommenders specialization, but this one was a RIDICULOUS amount of work to be done in 10 days. You research, experiment, and write a 10 page report in the first 7 days. Then you grade the first half of three other reports, and last half of three other reports in the next 3 days. If you decide to take this course, make sure to start at least a week early!
Keshaw S –
This was a very good course, or rather, an end to a fine specialization. When I started out, it felt like the job would be very easy since all kind of data were available. But it proved to be anything but. There was a lot of experimentation and mixing matching involved. But again, I believe this project has got more to do with doing good planning and choosing evaluation methods to meet business needs, rather than on implementation.
Subhajoy D –
The problem statement is well structured, and emulates a real life situation very well. From experience, I can assert that recommendation problems are pitched in a very similar manner as the one mentioned here. The guidelines assist in the student’s framing of the solution. One week however, might not be sufficient for completion from planning to execution to summarizing, especially if he/she is interested in the Honors version. In all, this is a must take for anyone to gain practical experience in this field, however, should start at it at least two weeks prior to the starting date, so that he/she doesn’t get hurried to complete it.