CSI5387
Concept Learning Systems/Machine Learning
Instructor
Nathalie Japkowicz
Office: STE 5-029
Phone: 562-5800 ext. 6693
E-mail: nat@site.uottawa.ca
Meeting Times and Locations
Office Hours and Locations
Overview
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.
Course Format
The course will consist of a mixture of regular lectures and student
presentations. The regular lectures 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.
Evaluation
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, homework and projects must
be submitted individually.
Pre-Requisites
Students should have reasonable exposure to Artificial Intelligence and
some programming experience in a high level language.
Required Textbooks
Additional References .
Other
Research papers will be available from Conference Proceedings or
Journals available from the Web.
(Links appear in the Syllabus table below, in the
List of Major Approaches
Surveyed
List of Theoretical Issues
Considered
List of Major Themes
Surveyed
·
Active
Learning
·
Anomaly
Detection
·
Graph
Mining
·
Evaluation
·
Discovery/Mining
·
Miscellaneous
Homework Related material:
· List
of Themes/Papers for this year
· Assignment
1 (due date: February 7, 2012; extended to February 14, 2012)
· Assignment
2 (due date: March 6, 2012)
· Assignment
3 (due date: March 27, 2012)
Course Support:
· Suggested
Outline for Paper Commentaries
· Guidelines
for the Final Project Report
·
R Code from the Japkowicz and Shah Evaluation Book
Machine Learning Ressources on the Web:
· David Aha's Machine Learning Resource Page
· WEKA
· Free Book:
Information Theory, Inference, and Learning Algorithms, David MacKay
Syllabus:
Week |
Topics |
Readings |
Week 1:
Jan 9 |
Introduction 1: Organizational Meeting Introduction 2: Overview of Machine Learning |
Texts: |
Week 2:
Jan 16 |
Approach:
Versions Space Learning Additional Slides on: inductive
learning theory, version spaces, decision trees and neural nets Approach: Decision Tree Learning |
Texts: Witten
& Frank, Sections 4.3 & 6.1 |
Week 3:
Jan 23 |
Theoretical Issue: Experimental Evaluation of
Learning Algorithms I |
Texts: Japkowicz
& Shah, Chapters 3, 4 Witten
& Frank, pp. 223-235 |
Week 4:
Jan 30 |
Theoretical Issue: Experimental Evaluation of
Learning Algorithms II |
Text: Japkowicz & Shah, Chapters
5, 6 Theme
Readings:
|
Week 5:
Feb 6 Homework 1 DUE on Tuesday |
Approach: Bayesian Learning
|
Texts: Witten & Frank, Sections
4.2 and 6.7 |
Week 6:
Feb 13 |
Approach: Instance-Based Learning |
Texts: Witten & Frank, Sections 4.7 and 6.4 Theme Readings:
|
Week 7 Feb 20 |
STUDY BREAK |
STUDY BREAK |
Week 8:
Feb 27 |
Approach: Rule Learning/Association
Mining |
Texts: Witten & Frank, Sections
4.4 and 6.2 Theme
Readings:
|
Week 9:
March 5 |
Approach: Support Vector Machines Theme: Graph Mining |
Texts: Witten & Frank, Sections
4.6 and 6.3 Theme Readings:
|
Week
10: Mar 12 |
Approach: Classifier Combination |
Texts: Witten & Frank, Section 7.5 Theme Readings:
|
Week
11: Mar 19 |
Theoretical
Issue: Computational Learning Theory Approach: Unsupervised Learning |
Texts: See Tom Mitchell’s book Texts: Witten & Frank, Sections 4.8 and 6.6. Theme Readingss:
·
Semi-Supervised Feature
Importance Evaluation with Ensemble Learning, Barkia
Hasna, Elghazel Haytham, and Aussem
Alex, ICDM’11 |
Week
12: Mar 26 |
Approach: Genetic Algorithms Theme: TBA |
Texts: See Tom Mitchell’s book Theme Readings: TBA |
Week
13: Apr 2 |
|
|