CSCE 566/CMPS 499: Data Mining

Spring 2017 (1/11 - 4/28)

Time/Location: TR 9:30am - 10:45am, Oliver 116
Instructor

Xindong Wu
xwu@louisiana.edu
Prerequisites

One-semester programming in Java (or C++).
One-semester statistics/probability.
Textbook
Additional References


Course Description

Data mining is a broad area that integrates techniques from several fields including machine learning, statistics, pattern recognition, artificial intelligence, and database systems, for the analysis of large volumes of data. This course gives a wide exposition of these techniques and their software tools. Topics include: association analysis, classification, clustering, pattern discovery in sequential data, and Bayesian networks.

Advanced Topics for PhD Comprehensive Exams
Grading Policy (in Spring '17)

Two Assignments: C4.5 due Feb. 2 and Weka due on Feb. 16 10%
Mid-Term Exam (close-book and close-notes): Mar. 16 25%
Paper Presentations    15%
Essay (for CSCE 566 students only) 20%
Final Exam (close-book and close-notes): May 1, 8:00 - 10:30 am, Oliver 116 30%

Course Syllabus by Chapter

Useful Links


Comments to Xindong Wu (xwu@louisiana.edu)