  We consider the machine teaching problem in a classroom-like setting wherein
the teacher has to deliver the same examples to a diverse group of students.
Their diversity stems from differences in their initial internal states as well
as their learning rates. We prove that a teacher with full knowledge about the
learning dynamics of the students can teach a target concept to the entire
classroom using O(min{d,N} log(1/eps)) examples, where d is the ambient
dimension of the problem, N is the number of learners, and eps is the accuracy
parameter. We show the robustness of our teaching strategy when the teacher has
limited knowledge of the learners' internal dynamics as provided by a noisy
oracle. Further, we study the trade-off between the learners' workload and the
teacher's cost in teaching the target concept. Our experiments validate our
theoretical results and suggest that appropriately partitioning the classroom
into homogenous groups provides a balance between these two objectives.
