Fall 2014: Data Mining [INFS-755]

Professor: Carlotta Domeniconi, Rm 4424 ENG, carlotta\AT\cs.gmu.edu
Teaching Assistant: TBA
Prerequisites: Some programming experience is expected. Students should be familiar with basic probability and statistics concepts, and linear algebra.
Location and Time: We meet in Innovation Hall 206, T 7:20pm - 10:00pm
Textbook: Pang-Ning Tan, Michael Steinbach, and Vipin Kumar Introduction to Data Mining, Addison Wesley, 2006. Book's companion website
Useful material:

Overview on Linear Algebra

Andrew Moore's Tutorials: Collection of tutorials on topics of interest for this class
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.

Course Format:
Lectures by the instructor. Besides material from the textbook, topics not discussed in the book may also be covered. Research papers and handouts of material not covered in the book will be made available. Grading will be based on homework assignments, exams, and a project. Homework assignments will require some programming. Exams and homework assignments must be done on an individual basis. 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.

Grading:
Assignments: 15%
Midterm: 25%
Final: 25%
Project: 30%
Participation: 5%

Course Project:
The project gives you an opportunity to explore in depth a particular topic/area of the course that interests you. The topic of the project, of course, should be related to the material covered in class, but otherwise you are free to select the specific topic. Possible types of projects include:

An application research project: The project demonstrates the application of some techniques discussed in class in an application domain (e.g., text mining, bioinformatics, computer vision, image processing, artificial intelligence etc.). Properties, drawbacks, advantages of the used techniques are analyzed within the context of the explored application domain.

A theoretical or methodological research project: A study of different classes of models and approaches; proving either theoretically or experimentally properties of known algorithms; designing a new approach.