CSI5387 Concept Learning Systems/Machine Learning


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


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. This year, the focus will be on Big Data Analysis.


Students will be evaluated on short written commentaries and oral presentations of research papers (15%), on two homework assignments (25%), on a final exam (20%), and on a final class project of the student's choice (40%). 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. For the research paper commentaries, students will work in teams of 3 or 4. However, homework, presentations and projects must be submitted/done individually.


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

Required Textbooks

Additional References .


Class Notes

Class notes are available here.

Other Reading Material

Articles for the students to critique and present will be provided in a zipped directory by e-mail, or as they become available.

List of Major Approaches Surveyed

List of Theoretical Issues Considered

List of Major Themes Surveyed

All the themes surveyed in this course pertain to the currently very popular extension of machine learning known as “Big Data Analysis”. The themes surveyed will be long to the following list:

·         Graph Mining

·         Mining Social Networks

·         Data Streams Mining

·         Unstructured or Semi-Structured Data Mining

·         Data Mining with Heterogeneous Sources

·         Spatio-Temporal Data Mining

·         Issues of Trust and Provenance in Data Mining

·         Privacy in Data Mining


Homework Related material:

·         List of Themes/Papers for this year: The papers read by the class this year will be distributed by e-mail or on Blackboard. The theme for this year is Big Data Analysis. Each team of 3 or 4 students must submit their summary and critique every week that a chapter is read.

·        Presentations: Presentations will be done individually. Each student will speak for approximately 15 minutes and answer some of the students/instructor’s questions. The schedule for presentations is available here.

·        Assignment 1: Supervised Learning Part I, Unsupervised Learning, Evaluation Techniques Part I. Handed out on Monday of Week 5; to return on the Wednesday of Week 8.

·        Assignment 2: Supervised Learning Part II, Evaluation Techniques Part II, Association Mining. Handed out on the Wednesday of Week 8; to return on the Wednesday of Week 11

·        Final Exam: The final exam will take place in class, on the Monday of Week 14. (Last day of classes)


Course Support:

· Suggested Outline for Paper Commentaries

· Final Exam Review Sheet

· Project Description

· Guidelines for the Final Project Report


Machine Learning Ressources on the Web:

· David Aha's Machine Learning Resource Page

· UCI Machine Learning


· Free Book: Information Theory, Inference, and Learning Algorithms, David MacKay

· R Code from the Japkowicz and Shah Evaluation Book





Week 1:

Jan 12

Organizational Meeting, Theoretical and Practical Overview of Machine Learning, Philosophical roots of machine Learning 



·         Flach: Prologue, Chapters 1, 2

·         Japkowicz & Shah : Chapter 1

·         Class Notes : Week 1


Week 2:

Jan 19

 Versions Space Learning, Decision Tree Learning


·         Flach: Chapters  4, 5

·         Japkowicz & Shah: Chapter 2

·         Class Notes: Week 2 – Part I & Part II

Week 3:

Jan 26

Artificial Neural Networks, Bayesian Learning


·         Flach: Sections 7.1-2, Chapter 9

·         Class Notes: Week 3 – Part I & Part II



Week 4:

February 2

Experimental Evaluation of Learning Algorithms


·         Japkowicz & Shah: Chapters 3-6

·         Flach: Chapter 12

·         Class Notes: Week 4

Week 5

February 9

·         Instance-Based Learning and unsupervised learning

·         Weekly Theme: Introduction to Big Data Analysis

Homework 1: Handed out on Monday; Due: Wednesday, Week 8


·         Flach: Chapter 8

·         Class Notes: Week 5 – Part I & Part II

·         Weekly Theme papers

Week 6:

Feb 16



Week 7

Feb 23

·         Rule Learning/Association Mining,

·         Weekly Theme: Data Stream Mining


Project Proposal DUE on Wednesday


·         Flach: Chapter 6

·         Class Notes: Week 7

·         Weekly Theme papers

Week 8:

March 2

·         Support Vector Machines,

·         Weekly Theme: Mining Social Networks


Homework 1 DUE on Wednesday

Homework 2: Handed out on Wednesday; Due: Wednesday, Week 11


·         Flach: Chapter 7

·         Class Notes: Week 8

·         Weekly Theme papers

Week 9:

March 9

·         Classifier Ensembles,

·         Weekly Theme: Trust, Provenance and Privacy


·         Flach: Chapter 11

·         Class Notes: Week 9

·         Weekly Theme papers

Week 10:

March 16

·         Features

·         Weekly Theme: Applications of Big Data Analysis I: Science, Medicine and Cyber Security


·         Flach: Chapter 10

·         Class Notes: Week 10

·         Weekly Theme papers

Week 11:

March 23

·         Genetic Algorithms,

·         Weekly Theme: Applications of Big Data Analysis II: Industry and Business

Homework 2: DUE on Wednesday


·         Class Notes: Week 11

·         Weekly Theme papers

Week 12:

March 30



Week 13:

April 6


Wednesday: Review for the FINAL EXAM

Week 14:

April 13