Selecting courses in an open-curriculum education program is a difficult task for students and academic advisors. Course recommendation systems nowadays can be used to reduce the complexity of this task. To control the recommendation error, we argue that course recommendations need to be provided together with "statistical" confidence. The latter can be used for computing a statistically valid set of recommended courses that contains courses a student is likely to take with a probability of at least 1-[epsilon] for a user-specified significance level [epilsilon]. For that purpose, we introduce a generic algorithm for course recommendation based on the conformal prediction framework. The algorithm is used for implementing two conformal course recommender systems. Through experimentation, we show that these systems accurately suggest courses to students while maintaining statistically valid sets of courses recommended. [For the full proceedings, see ED607784.]