CLASSES OF INTEREST TO STUDENTS WHO WORK IN ROBOTICS

PORTLAND STATE UNIVERSITY Winter 2000

Systems Science Ph.D. Program MW 4:00-5:50

Professor Martin Zwick& SB2, room 104

725-4987 zwick@sysc.pdx.edu

see http://www.sysc.pdx.edu/struct.html



SySc 551/651: DISCRETE MULTIVARIATE MODELING In the course schedule, the course is called "General Systems & Cybernetics-I"

This 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.

Prerequisites: Background in probability/statistics. SySc 511 is desirable but not essential.

TEXTS (1-2 at bookstore; 3 [packet] at Smart Copy, 1915th SW 6th Ave, 227-6137)

1. Krippendorff, Klaus (K). Information Theory: Structural Models for Qualitative Data. Series: Quantitative Applications in the Social Sciences, Paper # 62, Sage Publications, Beverly Hills, California, 1986. (ISBN 0-8039-2132-2, paperback)

2. Knoke, David and Burke, Peter J. (K & B). Log-Linear Models. Series: Quantitative Applications in the Social Sciences, paper # 20. Sage Publications, Beverly Hills, California, 1980. (ISBN 0-8039-1492-X, paperback)

3. Xeroxed articles and selections from books.

Grades will be based on midterm and final exams and either a computational project (e.g., data analysis using DMM software or software development) or a theory-exploring paper.