Courses One Page Flyer Winter 2009
SySc 511: Systems Theory
M/W 4:40-6:30 pm, HH 104
Martin Zwick 725-4987 firstname.lastname@example.org
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 email@example.com
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: www.webct.pdx.edu/public/sysc514ww2/index.html
SySc 551/651: Discrete Multivariate Modeling
T/R 4:40-6:30 pm, HH 104
Martin Zwick, 725-4987 firstname.lastname@example.org
The course focuses on information theory as a modeling framework and as a tool for discrete multivariate analysis. The course presents set- and information-theoretic methods for studying static or dynamic (time series) relations among qualitative variables or among quantitative variables having unknown nonlinear relationships. In the "general systems" literature, this is known as "reconstructability analysis" (RA). RA overlaps partially with log-linear statistical techniques widely used in the social sciences; both are especially valuable in data-rich applications (but RA is not exclusively statistical). RA is highly relevant to the many interrelated "projects" which go under the names of data-mining, machine learning, knowledge discovery and representation, etc.
Applied to data analysis, RA allows the decomposition and compression of multivariate probability distributions (contingency tables) and set-theoretic relations (and mappings), as well as the composition of multiple distributions/relations. The methods are very general. They are valuable in the natural and social sciences and in engineering, business, or other professional fields whenever categorical variables are useful or linear models are inadequate. Applied to the conceptualization of "structure" and "complexity," these set- and information-theoretic ideas are foundational for systems science.
More information: Discrete Multivariate Modeling
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, see the course homepage.