MIT Department of Electrical Engineering & Computer Science

EECS Fall 1999 Catalogue Supplement

6.891 Machine Learning and Neural Networks (H)

WF 11-12:30, 3-442, T 12, 34-302, T 4 34-301
Prof. Paul Viola, NE43-733, x8828
Prerequisite: 6.041, 18.313 (or 6.042 with permission of instructor)
4-0-8

This subject covers progress in machine learning and neural networks starting from perceptrons and continuing to recent work in "bayes nets" and "support vector machines". Basic algorithms, including backpropagation, Boltzmann machines, mixtures of experts, and hidden Markov models. Emphasis on the relationship to statistical inference. The main pre-reqs are probability and linear algebra. Students will find that having had either 6.034 or 6.011 will be helpful (i.e. familiarity with decision making and estimation in the presence of uncertainty and noise).