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

• Classification (Decision trees, Logistic regression, Support vector machines)
• Combining Multiple Learners (Bagging, boosting, cascading, stacking)
  
• Clustering (k-means, EM, hierarchical clustering, topic modeling)
  
• Dimensionality Reduction (Principal component analysis, linear discriminant analysis, subset selection)
• Graphical Models (Bayesian networks, Markov networks)

• Active learning
• Multi-label learning
• Graph mining
• Link prediction
• Data mining in bioinformatics
  
• Social media analytics
• Privacy-aware data mining
• Viral marketing
• Recommender systems
• Large scale data mining
• Temporal pattern mining
• Stream data mining
• Outlier detection

Course Information

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

Prerequisites

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.

• Assignments: 20%
• Midterm: 20%
• Paper Presentation: 20%
• Presenting one to two papers and leading the discussion in class.
  
• Project: 40%
• A project proposal, project presentation, and a final report.
  
• Final: There will not be a final exam in this class.

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