Restricted Boltzmann Machines (RBMs) are an unsupervised learning method (like principal components). An RBM is a probabilistic and undirected graphical model. They are becoming more popular in machine learning due to recent success in training them with contrastive divergence. They have been proven useful in collaborative filtering, being one of the most successful methods in the Netflix challenge (paper). Furthermore, they have been tantamount to training deep learning models, which appear to be the best current models for image and digit recognition.