CS 391 - Selected Topics: Data Mining
Course Information

Course Overview

Data mining describes a diverse area of research where techniques from machine learning, statistics, and information theory are synthesized to classify, cluster, interpolate, or associate large bodies of data.  The practitioner can "mine" such data for patterns or rules that facilitate greater understanding of the data and/or facilitate intelligent response to new data.  In this course, there are several learning objectives:


Data Mining: Practical Machine Learning Tools and Techniques (Second Edition)
Ian H. Witten, Eibe Frank
Morgan Kaufmann, June 2005
ISBN 0-12-088407-0


Todd Neller
Lecture: T,Th 1:10-2:25PM, Glatfelter 112
Office: Glatfelter 209
Office Hours: T,Th 10AM-12PM or by appointment. Note:  Generally, feel free to drop in if my office door is open.  If it is closed, I'm desperately seeking to keep on top of things and rabid attack ferrets may drop from the ceiling in my defense.
Phone: 337-6643


70% Assignments
10% Quizzes/Exams
20% Class Attendance/Participation

You are responsible to know the material from each lecture and reading assignment before the start of the next class.  Homework is due at the beginning of lecture on the due date.  Late homework will not necessarily be accepted.  Code must be a legal program in the relevant language in order to be graded.  (It need not be free from logic errors.)  For compiled languages, this means that the program must compile without error.  For interpreted languages, this means it must be interpretable without error.  Class attendance and participation is required.  If you attend all classes and are willing to participate, you'll get 100% for this part of your grade.  Even if you know enough to give a particular lecture, please consider the value of helping your peers during in-class exercises

Honor Code

Honesty, Integrity, Honor.  These are more important than anything we will teach in this class.  Students can and are encouraged to help each other understand course concepts, but all graded work must be done independently. The work you submit (including both code and problem solving ideas expressed in the code) should be your independent work.  For detailed information about the honor code, see http://www.gettysburg.edu/about/offices/provost/advising/honor_code/index.dot .