MIT Department of Electrical Engineering & Computer Science

EECS Fall 1998 Catalogue Supplement

6.893 Machine Learning and Neural Networks (H)

WF 11-12:30 and R 4, 31-161
Prof. Paul Viola, NE43-733, x8828
Prerequisite: 6.041, 6.042J or 18.313
4-0-8

Covers progress in machine learning and neural networks starting from perceptrons and continuing to recent work in "bayes nets" and "support vector machines." Explores basic algorithms, including backpropagation, Boltzmann machines, mixtures of experts, and hidden Markov models. The relationship to statistical inference will be emphasized. Students will find that having had either 6.034 or 6.011 will be extremely helpful (i.e., familiarity with decision making and estimation in the presence of uncertainty and noise).