Master recommender systems.. Learn to design, build, and evaluate recommender systems for commerce and content.
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.
Can I just enroll in a single course?
Can I take the course for free?
此课程是 100% 在线学习吗？是否需要现场参加课程？
Basic statistics or college algebra, and an ability to work with spreadsheets. For the honors track, you should also be comfortable implementing software in Java.
Do I need to take the courses in a specific order?
While each component can be useful by itself, the courses do build on each other and should be taken in order.
Will I earn university credit for completing the Specialization?
The University of Minnesota does not offer credit for completing this specialization. If you are enrolled elsewhere, you may wish to speak with your advisor or program staff to find out whether this specialization could be used for independent study credit.
You will understand and be able to apply the major families of recommender algorithms: non-personalized, product association, content-based, nearest-neighbor, and matrix factorization. You will know and be able to apply a variety of recommender metrics, and will be able to use this knowledge to match the correct recommender system to appplications.
What is the honors track?
The honors track is an optional track where learners add programming recommenders in the open source LensKit toolkit. You should be comfortable with basic data structures, algorithms, and Java to attempt the honors track.
How does this Specialization relate to the prior Recommender Systems courses?
This specialization is an extended and updated version of the two prior versions of Introduction to Recommender Systems that we've offered through Coursera. About 50% of the video and 80% of the assessment material are new, and there is an honors track with programming assignments (which existed in the first version of the course only, and have been re-done for this specialization). The Capstone is entirely new.