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 )

        Schedule of Presentations

        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]

Project Description

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

UCI Machine Learning

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

Homework 1 HANDED OUT on Monday

Theoretical Issue: Experimental Evaluation of Learning Algorithms I


Approach: Artificial Neural Networks

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


Theme: Big Data Analysis

Text: Japkowicz & Shah, Chapters 5, 6

Theme Reading: Reading List

Week 5:

Feb 3

Homework 1 DUE on Monday

Approach: Bayesian Learning



Theme: Multi-Label Data

Texts:

         Flach: Chapter 9

         Japkowicz & Shah: Chapter 7

Theme Reading: Reading List

Week 6:

Feb 10

Project Proposal DUE on Monday


Homework 2 HANDED OUT on Monday

Approach: Instance-Based Learning


Theme: Multi-View Data

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

Theme: Outlier Detection

Text: Flach: Chapter 6

Theme Reading: Reading List

Week 9:

March 3

Homework 2 DUE on Monday

Approach: Support Vector Machines

Theme: Text Mining

Text: Flach: Chapter 7

Theme Reading: Reading List

Week 10:

Mar 10

Homework 3 HANDED OUT on Monday

Approach: Classifier Combination


Theme: Data Mining for Health Informatics



Texts: Flach: Chapter 11

Theme Reading: Reading List

Week 11:

Mar 17

Theoretical Issue: Computational Learning Theory




Approach: Unsupervised Learning

Theme: Data Mining for Defense and Security

Text: Flach: Chapter 3

Theme Reading: Reading List

Week 12:

Mar 24

Homework 3 DUE on Monday

Approach: Genetic Algorithms



Theme: Social Network Analysis

Text: Flach Chapter 10 (Feature construction & selection)

Theme Reading: Reading List

Week 13:

Mar 31



Projects Presentation