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 email@example.com
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
First day of classes: Tuesday, August 30
No classes on Tuesday, October 11 (Columbus Day recess) and Thursday, November 24
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 %
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
Additional departmental CS information: http://cs.gmu.edu/wiki/pmwiki.php/HonorCode/CSHonorCodePolicies