Fall 2017: Theory and Applications of Data Mining [CS584] 

• Professor: Carlotta Domeniconi, Rm 4424 ENG, carlotta\AT\cs.gmu.edu 
• Teaching Assistant: Priya Mani
• Prerequisites: CS310 and STAT344 (C or better in both).
  Programming experience is expected. Students should be familiar with basic probability and statistics concepts, and linear algebra. 
• Location and Time: We meet in the Music/Theater Building 1005, W 4:30pm - 7:10pm 
• Textbook: P. N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Addison Wesley, 2006. Book's companion website
  
• Course Web Page
  

General Description and Preliminary List of Topics 

Data mining is the process of automatically discovering useful information in large data repositories. The course covers key concepts and algorithms at the core of data mining.

Topics include: classification, clustering, association analysis, anomaly detection. 

Grading

Assignments: 15% 

Midterm: 25% 

Final: 25% 

Project: 35% 

Exams are closed book. Assignments must be performed individually. Group work is NOT allowed, unless otherwise stated by the instructor. Any deviation from this policy will be considered a violation of the GMU Honor Code In addition, the CS department has its own Honor Code policies. Any deviation from this is also considered an Honor Code violation.