Prerequisites: Grade of C or better in CS 310 and STAT 344. Prerequisite enforced by registration system (see http://catalog.gmu.edu/preview_course_nopop.php?catoid=17&coid=108697)
Instructor: Prof. Harry
Wechsler wechsler@gmu.edu
Email correspondence using GMU accounts with subject CS 484
Course Description – Learn basic principles and methods for data analysis and knowledge discovery. Emphasis is to develop basic skills for modeling, prediction and performance evaluation. Topics include system design; data quality, preprocessing, and association; event classification; clustering; biometrics; business intelligence; and mining complex types of data.
Course (ABET) Outcomes:
1.
The
ability to apply computing principles, probability and statistics relevant to
the data mining discipline to analyze data.
2.
A
thorough understanding of model programming with data mining tools, algorithms
for estimation, prediction, and pattern discovery.
3.
The
ability to analyze a problem, identifying and defining the computational
requirements appropriate to its solution: data collection and preparation,
functional requirements, selection of models and prediction algorithms,
software, and performance evaluation.
4.
The
ability to understand performance metrics used in the data mining field to
interpret the results of applying an algorithm or model, to compare methods and
to reach conclusions about data.
5.
The
ability to communicate effectively to an audience the steps and results
followed in solving a data mining problem (through a term project).
Time, Day, and Venue: TR – Tuesday/Thursday, 3:00 – 4:15 pm
– Art and Design Building 2026
Office Hours: T 2:00 – 2:45 pm and R 4:30 – 5:15 or by appointment: ENGR 4448
http://registrar.gmu.edu/calendars/fall-2016-semester/
First day of classes: Tuesday, August 30
No classes on Tuesday, October 11 (Columbus Day recess) and Thursday, November 24
http://registrar.gmu.edu/calendars/fall-2016-semester/final-exams/
Final Exam: Thursday, December 15, 1:30 – 4:15 pm
Required Textbook: P. N. Tan, M.
Steinbach, and V. Kumar, Introduction
to Data Mining, Addison Wesley, 2006. http://www-users.cs.umn.edu/~kumar/dmbook/index.php
Reference Textbook (including online slides): I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning
Tools and Techniques (3rd ed.), Morgan Kaufmann, 2011. http://www.cs.waikato.ac.nz/ml/weka/book.html
CLOSED BOOK and NOTES
EXAMINATIONS
·
Homework
– 30%
·
(Non-Cumulative)
MidTerm1 and MidTerm2 –
Thursday, October 6 & Tuesday, November 22 – (10% each) –
20 %
·
REVIEW
for FINAL and TERM PROJECT – December
1
·
Team
Term Project – December 6 and 8 – 30
%
·
(Cumulative)
Final – December 15 –
20 %
http://www.fcps.edu/southcountyhs/sservices/gradescale.html
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-2/
Additional departmental CS information: http://cs.gmu.edu/wiki/pmwiki.php/HonorCode/CSHonorCodePolicies