Concepts and techniques in data mining and multidisciplinary applications. Topics include databases; data cleaning and transformation; concept description; association and correlation rules; data classification and predictive modeling; performance analysis and scalability; data mining in advanced database systems, including text, audio, and images; and emerging themes and future challenges.
Friday 10:30am-1:10pm
Exploratory Hall L004
Dr. Jessica Lin
Email: jessica [AT] gmu [DOT] edu
Office Hours: TBA
TBA
There will be 4-5 competition-style programming assignments. The preferred programming language is Python. Competition winners will get 1% extra credit added to the final grade.
There will be one midterm and one final exam. The final exam is comprehensive. Both exams are closed-book, and they must be taken at the scheduled time and place, unless prior arrangement has been made with the instructor. Missed exams cannot be made up.
You will be able to earn class participation credit through
in-class activities and quizzes. The purpose of the quizzes is to help
you stay on track of the lecture materials, so they are typically
short and easier compared to the midterm and final.
Required: Introduction
to Data Mining by Pang-Ning Tan, Michael Steinbach, and Vipin
Kumar (click on the link for the companion website)
The GMU Honor Code is in effect at all times. In addition, the CS Department has further honor code policies regarding programming projects, which are detailed here. Any deviation from the GMU or the CS department Honor Code is considered an Honor Code violation. All assignments for this class are individual unless otherwise specified.
If you have a documented learning disability or other condition which may affect academic performance, make sure this documentation is on file with the Office of Disability Services and then discuss with the professor about accommodations.