Data mining is the process of automatically discovering useful information from large data sets or databases. This course will provide an introduction to the main topics and algorithms in data mining and knowledge discovery, including: association discovery, classification, clustering, outlier detection, database support, and so on. Emphasis will be placed on the algorithmic and systems issues, as well as application of mining in real-world problems. Students will have to solve some small written and programming assignments that will help them to understand and digest the covered material.
Prof. George Kollios, email@example.com
Office: MCS 288
Office Hours: Monday 2:30 pm - 4:00 pm and Tuesday 10:25 am - 11:55 am, or by appointment.
Working knowledge of programming and data structures (CS 112, or equivalent). Familiarity with linear algebra, probability and statistics.
MW 4:00pm-5:30pm in MCS B33
Midterm: October 29, 2007, in class.
Final: December 17, 2007, 4:00 - 6:00 pm