Prerequisite: STAT 344 and INFS 614.
Instructor: Prof. Harry Wechsler http://cs.gmu.edu/~wechsler/
Time, Day, and Venue: T – Tuesday, 4:30 pm - 7:10
pm, Innovation Hall 204
[no class
on Tuesday, October 13, due to Columbus Day]
[last day of classes, Tuesday, December 8]
http://registrar.gmu.edu/calendars/2009Fall.html
Office Hours: Tuesday, 3:15 pm – 4:15 pm (ENGR - 4448)
Textbook:
Tan, Steinbach and Kumar, Introduction
to Data Mining,
Pearson / Addison Wesley, 2006
textbook slides: http://www-users.cs.umn.edu/~kumar/dmbook/
References
WEKA web site
for data mining software
http://www.togaware.com/datamining/survivor/Weka.html
UCI Machine
Learning Repository Content Summary
http://www.ics.uci.edu/~mlearn/MLSummary.html
Syllabus:
á
databases, data warehousing, data mining, knowledge discovery, and the Semantic
Web (W3C) http://www.w3.org/2001/sw
á
prediction,
model selection, validation, and performance evaluation
á
data
exploration
á
data
reduction and transformation
á
classification
á
association
á
clustering
á
anomaly
detection
á
applications:
biometrics and social networks
á
Homework
Assignments – 15 %
á
Mid Term
– June 9 – 25 %
á
Term TEAM
Project – 30 %
á
Final – http://registrar.gmu.edu/calendars/200970_exam.pdf
– Tuesday, December 15 – 30 %
You are expected to abide by the GMU honor code. Homework
assignments and exams are individual efforts. Information on the university
honor code can be found at http://academicintegrity.gmu.edu/honorcode/.
Additional departmental CS information: http://cs.gmu.edu/wiki/pmwiki.php/HonorCode/CSHonorCodePolicies