CSci6903 Special Topics
Machine Learning


Nathalie Japkowicz

Meeting Times and Locations

Office Hours and Locations


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.


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.


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:





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


Decision Tree Learning

Texts: Mitchell, Chapter3

Nilsson, Chapter 6

Papers: Friedman, Kohavi & Yun, 1996

Bradford et al., 1998

Mehta et al., 1995


Artificial Neural Networks

Texts: Mitchell, Chapter 4

Nilsson, Chapter 4

Papers: Fahlman & Lebiere, 1990

Pomerleau, 1991

Baluja, 1998


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


Bayesian Learning

Texts: Mitchell, Chapter 6

Nilsson, Chapter 5

Papers: Joachims, 1996

Niedermayer, 1998.


Instance-Based Learning

Texts: Mitchell, Chapter 8

Papers: Kasif et al., 1998

Atkeson et al., 1997

Ozturk & Aamodt, 1997


Computational Learning Theory

Texts: Mitchell, Chapter 7

Nilsson, Chapter 8

Papers: Kearns et al. 1991

Haussler et al, 1997


Rule Learning/Inductive Logic Programming

Texts: Mitchell, Chapter 10

Nilsson, Chapter 7

Papers: Bratko & Muggleton, 1995

Muggleton, 1998,

Cohen & Singer, 1999


Unsupervised Learning

Texts: Nilsson, Chapter 9


Fisher, 1996

Nigam et al., 1999

Kohonen, 1998


Genertic Algorithms

Texts: Mitchell, Chapter 9

Papers: Koza et al., 1998

Spector & Luke, 1996,

Hohn, 1997


Combining Classifiers, Mixture Models

Papers: Breiman, 1996

Schapire, 1999

Bauer & Kohavi, 1999

Jacobs et al., 1991

Shimshoni & Intrator, 1995


Projects Presentation

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