Prerequisites: CS 580
Instructor: Prof. Harry
Wechsler wechsler@gmu.edu
Course Description
– Course
covers concepts and techniques in data analytics for multidisciplinary applications.
Topics include basics in probability, statistics, and information theory; model
selection, prediction, and training and validation strategies; data
representation, cleaning, and transformation; data classification and discrimination;
association and correlation rules; data grouping and clustering; competitive
learning and self-organization; ensemble and voting methods; performance
evaluation; data mining supporting diverse applications including text
(“documents”), security (“spam and phishing detection”), and images
(“biometrics”); and emerging themes and future challenges (lack of annotation,
unbalanced populations, uncontrolled settings, anomaly detection, and
interoperability). Team (term) project required.
Time, Day, and Venue: W – Wednesday, 4:30
– 7:10 pm,
Exploratory
Hall L111
Office Hours: W – Wednesday, 3:00
– 4:00 pm, ENGR 4448
TA: Tanwistha
Saha tsaha@masonlive.gmu.edu : Time&Venue: TBD
http://registrar.gmu.edu/calendars/2014spring/
First
day of classes: Wednesday, January 22
Spring
Break [March 10 – 16]: no class on Wednesday, March 12
Last day of classes: Wednesday, April 30
http://registrar.gmu.edu/calendars/2014spring/exams/
Final Exam:
Wednesday, May 7, 4:30 – 7:15 pm
Office Hours: Wednesday, 3:00 – 4:00 pm (ENGR - 4448)
Textbook:
Data Mining by
Witten, Frank, and Hall, 3rd ed., Elsevier 2011.
Textbook Website (including slides): http://www.cs.waikato.ac.nz/~ml/weka/book.html
·
Homework
– 30%
·
Mid
Term – Wednesday, March 5 – 20 %
·
Team
Project – Class Presentation, April
23 & 30 – 30%
·
Final – Wednesday, May 7 - 20 %
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://oai.gmu.edu/the-mason-honor-code/
Additional departmental CS information: http://cs.gmu.edu/wiki/pmwiki.php/HonorCode/CSHonorCodePolicies