This specialization introduces the fundamental techniques used in recommender systems, a process that seeks to predict user preferences. Intended for people with related experience, it serves professionals like the data-mining expert who wants to use techniques such as collaborative filtering, as well as the data-literate marketing guru who wants to become more familiar with recommender systems. The courses offer interactive exercises to master different algorithms and there is an honors track for those who would like to go into greater depth. A case study involving the selection and design justification of a specific recommender system—and the analysis of its goals and algorithmic performance—is assigned as the capstone project.
The specialization takes approximately five months to complete at the suggested pace of three hours per week. All U of M Coursera courses, including those that are part of this specialization, may be taken individually. Coursera for Minnesota gives U of M students, faculty, and staff free access to courses and specializations.