Nathalie Japkowicz
Office: STE 5-029
Phone: 562-5800 ext. 6693
E-mail: nat@site.uottawa.ca
Machine Learning is the area of Artificial Intelligence concerned with the problem of building computer programs that automatically improve with experience. The intent of this course is to present a broad introduction to the principles and paradigms underlying machine learning, including presentations of its main approaches, discussions of its major theoretical issues, and overviews of its most important research themes.
The course will consist of a mixture of regular lectures and student presentations. The regular lectures, based on the textbook, will cover descriptions and discussions of the major approaches to Machine Learning as well as of its major theoretical issues. The student presentations will focus on the most important themes we survey. These themes will mostly be approached through recent research articles from the Machine Learning literature.
Students will be evaluated on short written commentaries and oral presentations of research papers (20%), on a few homework assignments (30%), and on a final class project of the student's choice (50%). For the class project, students can propose their own topic or choose from a list of suggested topics which will be made available at the beginning of the term. Project proposals will be due in mid-semester. Group discussions are highly encouraged for the research paper commentaries and students will be allowed to submit their reviews in teams of 3 or 4. However, homeworks and projects must be submitted individually.
Students should have reasonable exposure to Artificial Intelligence and some programming experience in a high level language.
Additional References .
Research papers will be available from Conference Proceedings or Journals available from the Web.
(Links appear in the Syllabus table below, in the
Week |
Topics |
Readings |
Week 1: Jan 4-8 |
Introduction: Organizational Meeting |
|
Week 2: Jan 9-15 |
Introduction: Overview of Machine Learning Approach: Versions Space Learning |
Texts: Texts: |
Week 3: Jan 16-22 |
Approach: Decision
Tree Learning |
Texts: Background for the Theme: Witten & Frank, Section 7.1 [Also, Chapter 2] Theme Papers:
|
Week 4: Jan 23-29 |
Theoretical Issue: Experimental
Evaluation of Learning Algorithms |
Texts: Theoretical Issue Papers:
|
Week 5: Jan 30- Feb 5 |
Approach: Artificial Neural Networks
|
Texts: Witten & Frank, pp. 223-235 Papers:
|
Week 6: Feb 6 - 12 |
Approach: Bayesian
Learning |
Texts: Witten & Frank, Sections 4.2 and 6.7 Theme Papers: |
Week 7: Feb 13 - 19 |
Approach: Instance-Based
Learning |
Texts: Witten & Frank, Sections 4.7 and 6.4 Theme Papers:
|
Week
8: Feb 20 - 26 |
STUDY BREAK |
STUDY
BREAK |
Week 9: Feb 27 - Mar 5 |
Approach: Rule Learning Theme: Mining Association Rules |
Texts: Witten & Frank, Sections 4.4 and 6.2 Theme Papers:
|
Week 10: Mar 6 - 12 |
Approach:
Support Vector Machines |
Texts: Witten & Frank, Sections 4.6 and 6.3 Theme Papers:
|
Week 11: Mar 13 - 19 |
Approach: Classifier Combination Theme: Classifier Parallelization |
Texts: Witten & Frank, Section 7.5 Papers: ·
Strategies for Parallel
Data Mining, David Skillicorn
|
Week 12: Mar 20 - 26 |
Theoretical Issue: Computational
Learning Theory |
Texts: See Tom Mitchell’s book Theme Papers: |
Week 13: Mar 27 – Apr 2 |
Approach: Unsupervised Learning Approach: Genetic
Algorithms |
Texts: Witten & Frank, Sections 4.8 and 6.6. Texts: See Tom Mitchell’s book |
Week 14: Apr 3 – 9 |
Projects Presentation |
|
Week 15: Apr 10 |
Projects Presentation |
|