CSI5387 Concept Learning Systems/Machine Learning

Instructor

Nathalie Japkowicz
Office: MCD 325-C
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, 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.

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 begining 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.

Pre-Requisites

Students should have reasonable exposure to Artificial Intelligence and some programming experience in a high level language.

Required Textbooks

Other Reading Material

Research papers will be available from Conference Proceedings or Journals available from the Library or from the Web. Alternatively, they will be distributed in class.

List of Major Approaches Surveyed

List of Theoretical Issues Considered

List of Major Themes Surveyed

Course Support:

Machine Learning Ressources on the Web:

Syllabus:

Week

Topics

Readings

Week 1: Sept 4-7

Organizational Meeting

Week 2: Sept 10-14

Overview of Machine Learning



Approach: Versions Space Learning

Texts:
Mitchell: Chapter 1
Nilsson: Chapter 1, Chapter 2

Texts:
Mitchell: Chapter 2
Nilsson: Chapter 3

Week 3: Sept 17-21

Homework 1 HANDED OUT on Monday

Approach: Decision Tree Learning



Theme: Feature Selection

Texts:
Mitchell, Chapter3
Nilsson, Chapter 6

Theme Papers:

Week 4: Sept 24-28

Thursday: Class Cancelled. Article reviews are due on Monday October 1.

Approach: Artificial Neural Networks



Theme: Cost-Sensitive Learning

Texts:
Mitchell, Chapter 4
Nilsson, Chapter 4

Theme Papers:

Week 5: Oct 1-5

Homework 1 DUE on Monday

Theoretical Issue: Experimental Evaluation of Learning Algorithms

No Theme this week: Papers discuss the theoretical issue

Texts:
Mitchell, Chapter 5

Papers:

Week 6: Oct 8-12

No lecture on Monday: Thanksgiving

Project Proposal DUE on Thursday

Homework 2 HANDED OUT on Thursday

Approach: Bayesian Learning


Theme: Learning from Massive Data Sets

Texts: Mitchell, Chapter 6
Nilsson, Chapter 5

Theme Papers:

Week 7: Oct 15-19

Approach: Instance-Based Learning

Theme: Combining Classifiers, Mixture Models I

Texts: Mitchell, Chapter 8

Theme Papers:

Week 8: Oct 22-26

Homework 2 DUE on Monday

Computational Learning Theory



Theme: Combining Classifiers, Mixture Models II

Texts: Mitchell, Chapter 7

Nilsson, Chapter 8

Theme Papers:

Week 9: Oct 29-Nov 2

Homework 3 HANDED OUT on Monday

Rule Learning/Inductive Logic Programming



Theme: Incorporating Domain Knowledge

Texts: Mitchell, Chapter 10

Nilsson, Chapter 7

Theme Papers:

Week 10: Nov 5-9

 Approach: Unsupervised Learning

No Theme this week: Papers discuss the approach

Texts: Nilsson, Chapter 9

Papers:

Week 11: Nov 12-16

Homework 3 DUE on Monday

Genetic Algorithms

Theme: Data Visualization

Texts: Mitchell, Chapter 9

Theme Papers:

Week 12: Nov 19-23

Approach: Support Vector Machines

Approach Papers:

Week 13: Nov 26-29

Projects Presentation

Week 14: Dec 3

Projects Presentation