I'll give a short introduction to Boltzmann machines and restricted Boltzmann machines. I'll discuss maximum likelihood learning in RBMs, and focus on several alternatives including contrastive divergence, pseudo-likelihood, ratio matching, and generalized score matching. I'll present a comparison of the resulting learning algorithms on several data sets and tasks including log likelihood evaluation, classification, de-noising, and novelty detection. I'll also be showing a live, interactive demo of all of the methods on a hand written digit recognition task. Everyone is welcome to try out the demo following the talk.