The best exercise I had in our sensing & estimation class was to derive the Kalman filter from 'scratch'. It's actually just the series of steps used to perform a minimization of a convex cost function, where the cost function is the inverse likelihood of the measurements given the data.

Someone once joked that all of sensing and estimation (and path planning and optimal control) is just applied convex minimization.