LM101-022: How to Learn to Solve Large Constraint Satisfaction Problems

In this episode we discuss how to learn to solve constraint satisfaction inference problems. The goal of the inference process is to infer the most probable values for unobservable variables. These constraints, however, can be learned from experience. At the end of the episode, we discuss one (unproven) theory from the field of neuroscience that our "dreams" are actually neural simulations of variations of events we have experienced during the day and "unlearning" of these dreams helps us to organize our memory! Visit us at: www.learningmachines101.com to obtain additional references, make suggestions regarding topics for future podcast episodes by joining the learning machines 101 community, and download free machine learning software!