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Courses One Page Flyer Winter 2011

SySc 511: Systems Theory

M/W 6:40-8:30 pm, HH 104

Tad Shannon 725-XXXX

SySc 511 surveys fundamental systems concepts and central aspects of systems theory. The course begins with an overview of the systems paradigm and the systems field as a whole. Topics then include introductions to set- and information-theoretic multivariate relations, dynamic systems, regulation and control, model representation and simulation; decision analysis, optimization, and game theory; artificial intelligence, complex adaptive systems. Readings draw from mathematics, the natural and social sciences, and the professional disciplines (e.g., engineering, business). The course content derives both from "classical" general systems theory, cybernetics, and operations research as well as from more contemporary systems research which is organized around the themes of nonlinear dynamics, complexity, and adaptation.

SySc 514: System Dynamics

Tu 6:30-10:00 pm, NH 437
Wayne Wakeland 725-4975

A lab and web-based course that introduces the student to the study of the dynamic behavior of continuous systems containing feedback. Vensim is the primary simulation language used in the course.

"Lecture" materials are provided on the web. Class time is used to assist students in carrying out various labs the reinforce the primary concepts. Some students may find that they can take the course almost entirely remotely.

More information:

SySc 575/675: AI: Neural Networks I

Neural networks is a computational and engineering methodology based on emulating how nature has implemented biological brain (in particular, the brain's massively parallel and learning aspects). As such, it holds promise for significant impact on how important classes of scientific and engineering problems are solved. The objective of the two-term sequence is to have the students obtain a working knowledge of this forefront technology.

This course covers basic ideas of the neural network (NN) methodology, a computing paradigm whose design is based on models taken from neurobiology and on the notion of "learning." A variety of NN architectures and associated computational algorithms for accomplishing learning are studied. Experiments with various of the available architectures are performed via a (commercial) simulation package. Students do a project on the simulator, or do a special programming project.

For more information:  Neural Networks I.