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Machine
Learning
(COSC 4P76)
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Brock University, Department of Computer
Science
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Instructor: Beatrice
Ombuki-Berman
Office:J307, x3494, office hrs: Tue: 2-3 pm
E-mail:bombuki@brocku.ca
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Prerequisites:
Creativity & completion of COSC 3P71
course
Course Description
This course is designed for advanced undergraduate students who have basic
knowledge about artificial intelligence. The primary objective of this course
is to introduce and study some basic principles, techniques, and applications
of a variety of learning models.
Some of the areas we will cover in this course include learning as search,
concept learning, inductive inference of decision trees, artificial neural
networks, genetic algorithms, bayesian learning, computational learning, and
reinforcement learning.
The emphasis of the course is on teaching the fundamentals, and not on
providing a mastery of specific commercially available software tools or programming
environments. Written and
programming assignments are used to help clarify basic concepts. The
student will learn research
skills applicable to other fields of computer science. A student will be
expected to understand and
present a selected current machine learning research paper and complete a
term project.
Evaluation
- Assignments (30%)
- in-class test (20%)
- Seminar(15%)
- Term project (30%)
- Class participation (5%)
Notes
- Assignments will be available on-line by the announced dates.
- There will be an in-class test on March 15, 2011
No end of term examination in this course.