MIT Department of Electrical Engineering & Computer Science

EECS Fall 1996 Catalogue Supplement

6.892 Statistical Computer Vision and Learning Theory (H)

TR 1-2:30, 36-372
3-0-9
Prof. Paul Viola, NE43-733 x8828

When most creatures are born they cannot see. Between birth and adulthood some set of changes occur that enable vision. Ever wonder what an infant's brain might be learning as it observes the world? Is there a simple theory that might explain the computations that underlie all of vision?

This course will cover a branch of computer vision and learning that is based on probabilistic analysis. Our primary focus will be on images, but we will be covering a number of more general subjects such as Bayesian decision theory, unsupervised learning, simulated annealing, and markov random fields (hidden and not hidden). In vision we will cover theories of unsupervised learning such as the Helmholtz (TM) machine, and Kohonen nets. We will ask and even try to answer the question, "What are edges for?" We will talk about supervised learning techniques such as neural networks, and radial basis functions.

Some theoretical knowledge of linear algebra and probability is a must. Interest in the brain a benefit. Some basic knowledge of computer vision and graphics will be assumed.