Ranked tag recommendation for university courses
This will most likely turn out to be multiple binary classification problems. 

Instead of building just one classifier, you would have to build as many classifiers as there are tags. Each classifier's task would be to predict whether a certain tag exists or not. A tag specific probability threshold can then be learnt by tuning over the precision-curve where course wise precision recall can be calculated. 

The percentile of the predicted probability for each tag can be used to obtain a ordered (sorted) list of tag suggestions. 

This way of converting a multi-class problem to multiple binary classification problems is known as binary relevance. While it is very efficient, it assumes that the labels themselves are independent. However, picking the probability threshold for each tag in order to optimize the course wise precision recall curve can lead to modeling dependence in the tags.