Ryerson University, Dept. of Computer Science

CPS 844: Data Mining
Course Management Form (Winter 2013)

Instructor 

Email 

Office 

Phone 

E. Harley

eharley@scs.
ryerson.ca

ENG 263

979 5000 x4874



Course homepage : Blackboard (my.ryerson.ca)
Office hours: Tuesdays 3-5, Wednesdays 3-5.
Calendar Course Description: Data Mining: This course introduces the basic data mining concepts, methods, implementations, as well as applications in different areas, especially on the world wide web. Topics covered include the basic data mining techniques, data preprocessing, association rule mining, classification, clustering, web mining, and data mining application (e.g. in web personalization, recommender system, security). At the end of this course, students should be able to implement and use some of important data mining algorithms in practical applications.
Synopsis: In this course we study fundamental concepts and algorithms of data mining, including data preprocessing, machine learning schemes (such as decision trees, classification rules, linear models, instance-based learning, clustering, Bayesian networks), and evaluation methods. In the lab work, we use the Waikato Environment for Knowledge Analysis (Weka), the software associated with the text.
Course Format: 3 weekly lecture hours plus one lab hour.

Text: Data Mining, Third Edition by Ian H. Witten, Eibe Frank and Mark Hall; Morgan Kaufmann Publishers, Elsevier. ISBN: 978-0-12-374856-0

Evaluation:

Item 

Percent

Tentative date

MidTerm Test

30%

week 8: Mar. 11 (in class)

Labs

10%

weekly

Participation

5%

weekly

Assignment

10%

tba

Final Examination

45% 

tba


General Information

  1. Results: Results for midterms and assignments will be returned within two weeks of due date.

  2. Posting of marks: Marks will be posted on Blackboard (my.ryerson.ca).

  3. Labs and Assignments: There will be approximately ten labs to be done individually and submitted on Blackboard or demonstrated to the TA. Labs will typically be exercises that help you become familiar with the Weka data mining software. The software is on the O drive in the lab, and also freely available on the web. There will also be an assignment/project.

  4. Participation: The participation mark is based on attendance of the graduate presentations (during the class time) and filling out a feedback form. This aspect of your grade is optional in the sense that you can ask to have the 5% weight put on your final exam.

  5. Plagiarism: Copied work (both copy and original) will be given a grade of zero.  Involvement with plagiarism can ultimately result in course failure and/or expulsion from the University in accordance with the Student Code of Academic and Non-Academic Conduct, Undergraduate Calendar. For more information on academic integrity, please also see www.ryerson.ca/academicintegrity).

  6. Announcements: Students are responsible for checking daily their Ryerson e-mail and announcements posted on the course home page, and for following all course related instructions so transmitted. 

Modifications to the course procedures will be made in consultation with the course students.  

Academic Consideration

http://www.ryerson.ca/content/dam/senate/forms/academic_consideration_document_submission.pdf


In addition, the following procedures must be followed as well:


For more detailed information on these issues, please refer to Senate Policy 134 at (Undergraduate Academic Consideration and Appeals) and Senate Policy 150 (Accommodation of Student Religious Observance Obligations). Both can be found at www.ryerson.ca/senate/policies/.