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Courses One Page Flyer Spring 2009

SySc 510: Models in the Natural & Social Sciences

This interdisciplinary course focuses on the concept of models in science. What is a model? How do models fit into the scientific method? What is common to all scientific models, regardless of discipline. What are the strengths and weaknesses of models as tools for understanding scientific problems? What makes one model better than another? What types of modeling techniques are available and which are best for investigating different types of scientific questions. These are the types of questions students will address by studying established models and by developing their own models in an area of their interest. The goal is for students to achieve both a better conceptual understanding of how models are used in science, as well as enhanced practical skills in using modeling methods and tools.

SySc 610: Discrete Multivariate Modeling II

From description of SySc 551/651 (Discrete Multivariate Modeling-I): In this course, information theory is used as a framework for modeling and data mining: for analyzing static or dynamic relations among discrete variables, for detecting complex interaction effects, and for discovering nonlinearities in continuous variables made discrete by binning. In the systems literature, these information-theoretic and related set-theoretic methods, used together with graph theory techniques, are called Reconstructability Analysis (RA). RA overlaps with and extends log-linear modeling in the social sciences, Bayesian networks and graphical models in machine learning, decomposition techniques in multi-valued logic design, Fourier methods for compression, and other modeling approaches. It can be used for confirmatory and exploratory statistical modeling as well as for non-statistical applications.

SySc 510/610 will continue the presentation of DMM (SySc 551/651), and will focus on (a) projects and (b) advanced topics. In projects, students will either do either (i) an intensive analysis of some dataset or (ii) a software project that enhances the current set of RA tools. The advanced topics will include most (or all) of the following: state-based RA and k-systems analysis; RA loopless models with many variables (Dependency Analysis); identification with inconsistent data; set-theoretic RA and binary decision diagrams; intra-model analysis; modeling with latent variables; RA and genetic algorithms; Fourier-based RA techniques; binning; the OCCAM software package.

SySc 527/627: Discrete System Simulation

The mathematical basis for discrete system simulation (DSS) is probability theory and queuing theory. It is used extensively in the fields of operations research, civil engineering, industrial engineering, systems analysis, etc. Students learn how to use DSS to model systems of interest.

SySc 553/653: Manufacturing System Simulation

The course focuses on using the ProModel discrete event simulation software to model manufacturing systems. Concepts include: a) overview of discrete system simulation and manufacturing simulation, b) data collection and prob. distributions, c) modeling material handling systems, d) job shop and production planning applications, and e) experimental design and output analysis. Relevant aspects of ProModel are also covered: locations, entities, processing logic, arrivals, path networks, resources, etc.

The course is designed to be of interest to students in Business, Engineering Management, Systems Science, Systems Engineering, and other programs; and to professionals in manufacturing, manufacturing engineering, and industrial engineering.

SySc 576: AI: Neural Networks II

MW 4:00-5:50, Room TBA
George Lendaris, 725-4988 lendaris@sysc.pdx.edu

Focuses on applications. Topics in fuzzy set theory, control theory, and pattern recognition are studied and incorporated in considering neural networks. A Design project (using NN simulator) in selected application area is done by each student.

More Information: Neural Networks II, Spring 2008