CS 294-1

Josephine Koh (jkoh@cs.berkeley.edu)
Sun, 14 Jan 1996 09:08:16 +0100

Spring 96

CS 294-1


Course Control No.: 24936 2-4 units
2 hours lecture

DESCRIPTION: This course provides an introduction to soft computing
-- a collection of methodologies which underlie the conception, design
and deployment of intelligent systems. The principal components of soft
computing are fuzzy logic, neural network theory, and probabilistic
reasoning, with the latter subsuming genetic algorithms, evidential
reasoning and belief networks. Within fuzzy logic, attention is focused
on the calculus of fuzzy if-then rules -- a basic tool which is employed
extensively in a wide variety of applications ranging from consumer
electronics to medical diagnostic systems. In combination with neural
network techniques, the calculus of fuzzy if-then rules provides a basis
for the design of neuro-fuzzy systems which have the capability to learn
and to adapt to changes in operating conditions. The basics of neural
network theory, genetic algorithms and probabilistic reasoning are
discussed and their roles in the applications of soft computing are
illustrated by practical examples.

PREREQUISITES: The course is self-contained. No prior knowledge
of fuzzy logic or neural network theory is required.

FINAL EXAMS: Term paper or programming project.

INSTRUCTOR IN CHARGE: L. A. Zadeh: 642-4959; zadeh@cs

TIME AND PLACE: Mondays 2-4 pm; 373 Soda Hall