Download 8 Name: Daniel L. Silver Title: Theory and Application of Machine

Name: Daniel L. Silver
Title: Theory and Application of Machine Learning
Area: Theory
Course Type: Cross-listed
Description:
Machine learning covers an area of Artificial Intelligence that focuses on the development of software that can
induce models directly from examples of a mathematical function or real-world phenomenon. Systems that employ
machine learning gain all or part of their knowledge by experiencing the world as opposed to a priori programming.
Furthermore, they are able to continue learning and, therefore, adapt as new examples are presented. Machine
learning techniques are now being employed in a wide range of areas, two of the most popular of these is data
mining (for the discovery of new knowledge in databases) and user modeling (for the purposes of information
filtering and software personalization).
The course covers (1) the theory of machine learning (such as inductive bias, PAC theory of learnability,
sample complexity), (2) several popular machine learning algorithms (Bayesian methods, inductive decision trees,
artificial neural networks and K-nearest neighbour), (3) their relative strengths and weaknesses and (4) their
application to data mining and user modeling problems (proper development and evaluation of induced models).
The theory will be reinforced through lab exercises where the students will have the opportunity to use machine
learning systems.
As a course project, a student will either develop a simple machine learning system or employ an existing
system as part of a data mining or user modeling application. Each implementation will be documented and
presented at the end of the course.
Course Material:
Text: Machine Learning (1st Ed.) by Tom Mitchell, ISBN: 0-07-042807-7, Publisher: McGraw Hill. Available in the
bookstore or from your favourite on-line source (Indigo, Amazon).
Software: Weka, SVM, Bayes, C4.5, NeuroShell2 (all free)
Prerequisites:
Comp 2663, 3613 and Math 1023 and 2433 or equivalents. An introductory course in statistics would be beneficial;
however the necessary material will be covered as the course proceeds. Java and C programming background will
be required for project work.
Enrolment:
No restrictions
Marking Scheme:
Theory (50%): The final examination will be a take-home. It will cover all material from the course and
demonstrate your knowledge and understanding of fundamental machine learning theory and of the course
assignments.
Course Project (25%) : 10% based on the difficulty and quality of the learning system developed. 15% based on
the quality if the project proposal, project report and presentation given in class.
Assignments and Quizzes (25%): There will be several assignments and inclass quizzes to ensure students are
keeping up with the material.
References:
[1] Schaum's Outline Series: Theory and Problems of Probability (available at the library and through the
bookstore).
[2] J.Ross Quinlan's book on c4.5 entitled "C4.5 Programs for Machine Learning".
[3] Machine Learning (1st Ed.) by Tom Mitchell, ISBN: 0-07-042807-7, Publisher: McGraw Hill.
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