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 Reading Material
Research papers will be available from Conference Proceedings or
Journals available from the Web.
(Links appear in the Syllabus table below, in the Readings column)
List of Major Approaches
Surveyed
List of Theoretical Issues
Considered
List of Major Themes
Surveyed
·
Big
Data Analysis
·
Multi-Label
Data Classification
·
Multi-view
Data Classification
·
Outlier
Detection
·
Text
Mining
·
Data
Mining for Health Informatics
·
Data
Mining for Defense and Security
·
Social
Network Analysis
Homework Related material:
· List of Themes/Papers for this year (Click here )
·
Assignment 1
(pdf)[courtesy of Ashwin]
·
Assignment 2 ( pdf)
·
Assignment 3 ( pdf )
Course Support:
· Suggested
Outline for Paper Commentaries
· Course Notes in pdf format [Courtesy of Ashwin]
· 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 6 |
Introduction 1: Organizational Meeting Introduction 2: Overview of Machine Learning |
Texts: ·
Flach: Prologue, Chapters 1, 2 ·
Japkowicz & Shah : Chapter 1 ·
|
Week 2:
Jan 13 |
Approach:
Versions Space Learning Additional Slides on: inductive
learning theory, version spaces, decision trees and neural nets Approach: Decision Tree Learning , PlayTennis Dataset |
Texts: ·
Flach: Chapters
4, 5 ·
Japkowicz & Shah: Chapter 2 |
Week 3:
Jan 20 |
Theoretical Issue: Experimental Evaluation of
Learning Algorithms I |
Texts: ·
Flach: Chapter 12 ·
Japkowicz & Shah, Chapters 3, 4 |
Week 4:
Jan 27 |
Theoretical Issue: Experimental Evaluation of
Learning Algorithms II and ROC Curve Illustrations |
Text: Japkowicz & Shah, Chapters
5, 6 Theme
Reading: Reading List |
Week 5:
Feb 3 Homework 1 DUE on Monday |
Approach: Bayesian Learning
|
Texts: ·
Flach: Chapter 9 ·
Japkowicz & Shah:
Chapter 7 Theme
Reading: Reading List
|
Week 6:
Feb 10 |
Approach: Instance-Based Learning |
Texts: ·
Flach: Chapter 8 ·
Japkowicz & Shah:
Chapter 8 Theme
Reading: Reading List |
Week 7 Feb 17 |
STUDY BREAK |
STUDY BREAK |
Week 8:
Feb 24 |
Approach: Rule Learning/Association
Mining |
Text: Flach: Chapter 6 Theme Reading: Reading List |
Week 9:
March 3 |
Approach: Support Vector Machines Theme: Text Mining |
Text: Flach: Chapter 7 Theme
Reading: Reading List |
Week
10: Mar 10 |
Approach: Classifier Combination
|
Texts: Flach: Chapter 11 Theme
Reading: Reading List |
Week
11: Mar 17 |
Theoretical
Issue: Computational Learning Theory Approach: Unsupervised Learning |
Text: Flach: Chapter 3 Theme Reading: Reading List |
Week
12: Mar 24 |
Approach: Genetic Algorithms Theme: Social Network Analysis |
Text: Flach Chapter 10 (Feature
construction & selection) Theme Reading: Reading List |
Week
13: Mar 31 |
|
|