CMPUT 695 - Knowledge Discovery and Data Mining
CMPUT 695 Syllabus
Course Home Page: http://www.cs.ualberta.ca/~zaiane/courses/cmput695/General Information
Graduate course
Term: Winter 2007
Date and Time: T 9:30 - 10:50 Th 9:30 - 10:50
Location: CSC B 41
Number of credits: 3 credits
Contact
Instructor: Osmar
Zaïane
Office: ATH 352
Phone: 492 2860
E-mail: zaiane @ cs.ualberta.ca
Office Hours: By mutually agreed upon appointment.
Course Objectives
Data Mining and Knowledge Discovery has become an active area of research, attracting people from several disciplines including: database systems, statistics, information retrieval, pattern recognition, AI/machine learning, and data visualization.
The course will introduce data mining and data warehousing, and study their principles, algorithms, implementations, and applications.
Pre-requisites
There are no pre-requisites per-se as the couse is self contained. However, an introductory course on Database Systems (CMPUT 391 or equivalent) is a must. Preferred (but not required): CMPUT-366 (An Introduction to Artificial Intelligence) and other courses on Database Systems, Machine Learning, Information Retrieval, and Statistics.
Course Topics
Course topics include:
- An introduction to data mining and data warehousing: motivation and applications;
- Basic and advanced data mining techniques: characterization, association, sequential patterns analysis, classificiation, clustering, outlier detection, and contrast set mining;
- Advanced data mining applications: mining relational and transaction data, mining time-related data, spatial data mining, textual data mining, multimedia data mining, visual data mining, and Web mining.
Course Work and Evaluation
| Course Work | Date | Weight |
|---|---|---|
| Assignment 1 | October 11nd, 2007 | 9% |
| Assignment 2 | Variable | 11% |
| Presentation | Variable | 16% |
| Midterm | October 25th, 2007 | 25% |
| Project | Week December 17th, 2007 | 39% |
Course Materials
No specific textbooks are recommended. The instruction is based on the lectures and provided lecture slides. Other published research papers and technical reports will also be provided.
Policy
Course Outlines
Policy about course outlines can be found in Section 23.4(2) of the University Calendar.
Academic Integrity
The University of Alberta is committed to the highest standards of academic integrity and honesty. Students are expected to be familiar with these standards regarding academic honesty and to uphold the policies of the University in this respect. Students are particularly urged to familiarize themselves with the provisions of the Code of Student Behaviour (online at www.ualberta.ca/secretariat/appeals.htm) and avoid any behaviour which could potentially result in suspicions of cheating, plagiarism, misrepresentation of facts and/or participation in an offence. Academic dishonesty is a serious offence and can result in suspension or expulsion from the University. (GFC 29 SEP 2003)
Collaboration
Collaboration is allowed and encouraged, but plagiarism is not. You must submit your own work and never claim to be yours what is not.
