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. This year, the focus will be on Big Data Analysis.
Evaluation
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.
Pre-Requisites
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 belong 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
Week 4; to return on Week 7.
·
Assignment 2: Supervised Learning Part II, Evaluation
Techniques Part II, Association Mining. Handed out on Week 7; to return on Week
10
·
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
· Guidelines
for the Final Project Report
Machine Learning Ressources on the Web:
· David Aha's Machine Learning Resource Page
· WEKA
· Free Book:
Information Theory, Inference, and Learning Algorithms, David MacKay
· R
Code from the Japkowicz and Shah Evaluation Book
Syllabus:
|
Week |
Topics |
Readings |
|
Week 1: Sep 9 |
Organizational
Meeting,
Theoretical and Practical Overview of Machine Learning, Philosophical roots of machine
Learning |
Texts: ·
Flach: Prologue, Chapters 1, 2 ·
Japkowicz & Shah : Chapter
1 ·
Class Notes : Week
1 ·
|
|
Week 2: Sep 16 |
Versions Space Learning, Decision Tree Learning |
Texts: ·
Flach: Chapters
4, 5 ·
Japkowicz & Shah: Chapter 2 ·
Class Notes: Week 2 – Part I & Part II |
|
Week 3: Sep 23 |
Artificial
Neural Networks, Bayesian Learning |
Texts: ·
Flach: Sections 7.1-2, Chapter 9 ·
Class Notes: Week 3 – Part I & Part II |
|
Week 4: Sep 30 |
Experimental
Evaluation of Learning Algorithms |
Texts: ·
Japkowicz & Shah: Chapters 3-6 ·
Flach: Chapter 12 ·
Class Notes: Week 4 |
|
Week 5 Oct 7 |
·
Instance-Based Learning and unsupervised learning ·
Weekly Theme: Introduction to Big Data
Analysis |
Texts: ·
Flach: Chapter 8 ·
Class Notes: Week 5 – Part I & Part II ·
Weekly Theme papers |
|
Week 6: Oct 14 |
·
Rule Learning/Association
Mining, ·
Weekly Theme: Data Stream Mining
|
STUDY BREAK |
|
Week 7 Oct 21 |
·
Support Vector Machines, ·
Weekly Theme: Mining Social Networks Homework 1 DUE today Project Proposal DUE today Homework 2: Handed out today; Due:
Wednesday, Nov 11 |
Texts: ·
Flach: Chapter 6 ·
Class Notes: Week 7 ·
Weekly Theme papers |
|
Week 8: Oct 28 |
STUDY BREAK |
Texts: ·
Flach: Chapter 7 ·
Class Notes: Week 8 ·
Weekly Theme papers |
|
Week 9: Nov 4 |
·
Classifier Ensembles, ·
Weekly Theme: Trust, Provenance and Privacy |
Texts: ·
Flach: Chapter 11 ·
Class Notes: Week 9 ·
Weekly Theme papers |
|
Week 10: Nov 11 |
·
Features ·
Weekly Theme: Applications of Big Data
Analysis I: Science, Medicine and Cyber Security |
Texts: ·
Flach: Chapter 10 ·
Class Notes: Week 10 ·
Weekly Theme papers |
|
Week 11: Nov 18 |
·
Genetic Algorithms, ·
Weekly Theme: Applications of Big Data Analysis II: Industry and Business |
Texts: ·
Class Notes: Week 11 ·
Weekly Theme papers |
|
Week 12: Nov 25 |
FINAL PROJECT PRESENTATIONS |
|
|
Week 13: Dec 2 |
FINAL EXAM FINAL PROJECT PRESENTATIONS |
|