CS522 Spring 2011: Advanced Data Mining

Course Description

In this course, we will cover both basic and advanced data mining techniques in depth (see possible list of topics below). The course will consist of a mixture of lectures by the instructor and presentations by the students. Each student is also expected to gain hands on experience by carrying out a semester long project on their topic of choice.

Course Topics

The following is a tentative list of topics that the instructor will cover:
The following is a tentative list of topics that the students are expected to pick a paper on and present:

Course Information

Time and Location: Tue - Thu 1:50 - 3:05pm in Stuart 220
Mustafa Bilgic
Office: Stuart 228C
Email: mbilgic AT iit.edu
Office Hours: Tue - Thu 11am - 12pm (Other times by appointment)


CS422 - Data Mining is a required prerequisite. However, if you have not taken CS422 and are still interested in taking the 01 section, please send an email to me. Depending on your background, I might waive the prerequisite. I will not, however, waive the prerequisite for the Televised and Internet sections.

Course Format and Grading

I will lecture the first half of the semester. In the second half of the semester, each student is expected to present one to two papers and lead the discussion on a topic of their choice. Students are expected to read the required materials, prepare a short write-up about the reading materials, and participate in the discussions. Students are also expected to carry-out a semester long project on a problem of their choice.

Course Material

There is a required text book for this course:

Machine Learning, 2nd edition, by Ethem Alpaydin

There will be additional reading materials (mostly available on the web).

Tentative Schedule

Week 1: Jan 10: Introduction
Week 2: Jan 17: Decision Trees
Week 3: Jan 24: Naive Bayes and Logistic Regression
Week 4: Jan 31: Support Vector Machines
Week 5: Feb 7: Bagging and Boosting
Week 6: Feb 14: Clustering
Week 7: Feb 21: Dimensionality Reduction and Feature Selection
Week 8: Feb 28: Graphical Models
Week 9: Mar 7: Review and Midterm
Week 10: Mar 14: SPRING BREAK
Week 11: Mar 21: Advanced Topics
Week 12: Mar 28: Advanced Topics
Week 13: Apr 4: Advanced Topics
Week 14: Apr 11: Advanced Topics
Week 15: Apr 18: Advanced Topics
Week 16: Apr 25: Project Presentations
Week 17: May 2: FINALS WEEK