Reinforcement Learning: Given sequences of inputs, actions from a
fixed set, and scalar rewards/punishments, learn to select action
sequences in a way that maximizes expected reward, e.g. chess and robotics. (This is more akin to learning how to design good experiments and is not covered in this course.)
Other stuff, like Preference Learning, learning to rank, etc. (also not covered in this course). Note that many machine learning problems can be (re-)formulated as special cases of either a supervised or unsupervised problem, which are both covered in this class.