Fall 2003 One Page Flyer

SySc 513: Systems Approach

Provides a practitioner-oriented introduction to systems, including:

  • observer dependencies & context
  • meta-systems & subsystems
  • value systems and associated optimization/sub-optimization
  • life-cycle project management
  • inquiring systems
  • learning organizations
  • multiple perspectives.

Also explores qualitative aspects systems analysis tools such as graphs, structural modeling, and dynamic modeling.

For more information: Systems Approach Course Description

SySc545/645: Information Theory

Covers the basic theoretical limits on the performance of source coding (compression) and channel coding (error correction) for discrete sources and channels.

Course Announcement at http://www.ee.pdx.edu/~andy/info1/announce.pdf.

Syllabus at http://www.ee.pdx.edu/~andy/info1/syllabus.pdf.

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 home page for Winter 2001

SySc 529/629: Business Process Modeling & Simulation

The primary emphasis is on using discrete system models to analyze administrative, decision-making, product development, manufacturing, and service delivery processes. Discrete system models characterize the system as a flow of entities that enter and move through various processes and queues according to probability functions specified by the modeler. Monte Carlo sampling is used to calculate statistical measures of system performance, such as throughput, average queue length, resource utilization, etc. Some processes may also exhibit continuous characteristics, in which case continuous model constructs may be deployed. Continuous system models utilize the numerical integration of differential equations to simulate behavior over time. Such models are often used for studying the systems containing feedback loops, where the outputs are "fed back" and compared with control inputs. Process measurement and the unique challenges of modeling the software development process will also be covered in some detail.

For more information: http://www.webct.pdx.edu/public/sysc529/index.html

SySc 525/625: Agent Based Simulation

This course focuses on the technical and theoretical aspects of agent-based programming. During this class students will learn how to use StarLogo to create agent-based models and use agent-based simulations in research and education. Reading assignments focus on the history and theories behind agent-based programming and the decentralized perspective.

For more information: http://www.webct.pdx.edu/public/sysc610abs/index.html