  In this work we study the problem of using machine-learned predictions to
improve the performance of online algorithms. We consider two classical
problems, ski rental and non-clairvoyant job scheduling, and obtain new online
algorithms that use predictions to make their decisions. These algorithms are
oblivious to the performance of the predictor, improve with better predictions,
but do not degrade much if the predictions are poor.
