CSci6903 Special Topics
Machine Learning

Instructor

Nathalie Japkowicz

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 discussions of each of the major approaches currently being investigated.

Course Format

The course will consist of a mixture of regular lectures and student presentations.

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.

Course Support:

Machine Learning Ressources on the Web:

Preliminary Syllabus:

Week

Topics

Readings

1

Overview of Machine Learning

Texts: Mitchell, Chapter 1

Nilsson, Chapter 1, Chapter 2

 

 

Concept Learning and the General-to-Specific Ordering

Texts: Mitchell, Chapter 2

Nilsson, Chapter 3

Papers: Ali, Brunk & Pazzani, 1994

Sebag, 1996

3

Decision Tree Learning

Texts: Mitchell, Chapter3

Nilsson, Chapter 6

Papers: Friedman, Kohavi & Yun, 1996

Bradford et al., 1998

Mehta et al., 1995

4

Artificial Neural Networks

Texts: Mitchell, Chapter 4

Nilsson, Chapter 4

Papers: Fahlman & Lebiere, 1990

Pomerleau, 1991

Baluja, 1998

5

Experimental Evaluation of Learning Algorithms

Texts: Mitchell, Chapter 5

Papers: Geman, Bienenstock & Doursat (1992). Neural Networs and the bias/variance dilemma. Neural Computation 4, 1-58. (Available from Killiam)

Dietterich, 1998

6

Bayesian Learning

Texts: Mitchell, Chapter 6

Nilsson, Chapter 5

Papers: Joachims, 1996

Niedermayer, 1998.

7

Instance-Based Learning

Texts: Mitchell, Chapter 8

Papers: Kasif et al., 1998

Atkeson et al., 1997

Ozturk & Aamodt, 1997

8

Computational Learning Theory

Texts: Mitchell, Chapter 7

Nilsson, Chapter 8

Papers: Kearns et al. 1991

Haussler et al, 1997

9

Rule Learning/Inductive Logic Programming

Texts: Mitchell, Chapter 10

Nilsson, Chapter 7

Papers: Bratko & Muggleton, 1995

Muggleton, 1998,

Cohen & Singer, 1999

10

Unsupervised Learning

Texts: Nilsson, Chapter 9

Papers:

Fisher, 1996

Nigam et al., 1999

Kohonen, 1998

11

Genertic Algorithms

Texts: Mitchell, Chapter 9

Papers: Koza et al., 1998

Spector & Luke, 1996,

Hohn, 1997

12

Combining Classifiers, Mixture Models

Papers: Breiman, 1996

Schapire, 1999

Bauer & Kohavi, 1999

Jacobs et al., 1991

Shimshoni & Intrator, 1995

13

Projects Presentation

(Note: Certain topics currently listed in the syllabus may be replaced by other topics such as Reinforcement Learning, Genetic Algorithms, etc.)