Courses

Summary: Find the useful information hidden in your data!  This course
surveys computer-intensive methods for inductive classification and
estimation, drawn from Statistics, Machine Learning, and Data Mining.

Data Mining Course: "Tools for Discovering Patterns in Data:  A Survey of
Modern Data Mining Algorithms", Charlottesville, Virginia, October 16-17,
2000, taught by John Elder, Ph.D., Elder Research, Inc.;  For more
information, please see www.datamininglab.com/newcourse.html

Course Description:

Find the useful information hidden in your data!  This course surveys
computer-intensive methods for inductive classification and estimation,
drawn from Statistics, Machine Learning, and Data Mining.  Dr. Elder will
describe the key inner workings of leading algorithms, compare their
merits, and (briefly) demonstrate their relative effectiveness on practical
applications.  We'll first review classical statistical techniques, both
linear and nonparametric, then outline the ways in which these basic tools
are modified and combined into more modern methods.  The course pays
particular attention to four powerful approaches:  Neural Networks,
Polynomial Networks, Kernels, and Decision Trees, and uses actual
scientific and business problems to illustrate useful accompanying
techniques (such as scientific visualization, resampling, and bundling)
employed by experienced analysts.  Along the way, major relative strengths
and distinctive properties of the leading commercial software tools for
Data Mining will be discussed.  For more information, please see
www.datamininglab.com/newcourse.html.

Intended Audience:
Those from industry and academia who work with data and wish to understand
recent developments in pattern discovery, data mining, and inductive
modeling. At the conclusion of this course, one should be able to discern
the basic strengths of competing methods and select the appropriate tools
for one's applications. Participants should have prior working experience
with computers and interest in applied statistical techniques. (It helps,
as well, to have a motivating application you wish to solve.)

Handouts:
Comprehensive notes, annotated references, and the book chapter, "A Statistical
Perspective on Knowledge Discovery in Databases", by John Elder & Daryl
Pregibon.

Instructor:
John Elder is Chief Scientist of Elder Research, a Data Mining consulting
firm in Charlottesville, Virginia. He has over fifteen years of experience
developing and applying adaptive, data-driven techniques to practical
problems.  Dr. Elder has written and spoken widely on pattern discovery and
has authored a handful of influential data mining programs. He is associate
editor of Statistics and Computing, is on the board of the Interface
Foundation, and chairs the Adaptive and Learning Systems Group of the
IEEE-SMC Society.

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Antonia de Medinaceli                Phone: 804.973.7673
Elder Research                          Fax:     804.995.0064
1006 Wildmere Place antonia@datamininglab.com
Charlottesville, VA 22901 www.datamininglab.com
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