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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



Instructor: Osmar Zaïane
Office: ATH 352
Phone: 492 2860
E-mail: zaiane @ cs.ualberta.ca
Office Hours: By mutually agreed upon appointment.

CMPUT 695 Newsgroup

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.


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:

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.


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 is allowed and encouraged, but plagiarism is not. You must submit your own work and never claim to be yours what is not.

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