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.