The course covered learning techniques for many different types of neural network including deep feed-forward networks, recurrent networks and Boltzmann Machines. It covered recent applications to speech, vision, and language, and used hands-on programming assignments.

Geoffrey Hinton was one of the researchers who introduced the back-propagation algorithm that has been widely used for practical applications. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning, products of experts and deep belief nets. He received his PhD in Artificial Intelligence from Edinburgh in 1978 and spent five years as a faculty member in Computer Science at Carnegie-Mellon. He then moved to the Department of Computer Science at the University of Toronto where he directs the program on "Neural Computation and Adaptive Perception” for the Canadian Institute for Advanced Research.