Spring 2008: Tuesdays and Thursdays 3:30pm - 4:45pm & Wednesdays
3:35pm - 4:25pm, Boyd GSRC 208
Instructor: Prof. Khaled
Office Hours: Tuesday: 1-2:30pm and Wednesday: 4:35-6:00pm or by email
Office Location: Room 219B, Boyd GSRC
Machine learning is a sub-field of artificial intelligence which is
concerned with computer programs that can automatically improve their
capabilities and/or performance by acquiring (learning) experience.
The main objectives of this course are to provide students with an
in-depth introduction to machine learning theory and methods
and an exploration of research problems in machine learning and its
applications which may lead to work on a project or a dissertation.
The course is intended primarily for computer science and artificial
intelligence graduate students. Graduate students from other
departments who have a strong interest and sufficient experience in
artificial intelligence may also find the course interesting.
CSCI/PHIL 4550/6550 Artificial Intelligence or CSCI 4560/6560
Evolutionary Computation (or permission of the instructor).
Familiarity with basic computer algorithms and data structures and at
least one high level programming language.
Topics to be Covered:
Part I: Machine learning techniques: Selected from inductive learning,
decision trees, neural network approaches, evolutionary computation
approaches and classifier systems, reinforcement learning, statistical
and Bayesian learning, instance-based learning, explanation-based
learning and computational learning theory.
Part II: Machine learning applications: Selected from data mining,
biomedical modelling, medical diagnosis, text classification, pattern
recognition and/or other contemporary applications.
Reading; assignments (some include programming and/or running existing
programs); midterm; final and term project and paper. (Unless
otherwise announced by the instructor: all assignments and all exams
must be done entirely on your own.)
Academic Honesty and Integrity:
All academic work must meet the standards contained in
"A Culture of Honesty." Students are responsible for informing
themselves about those standards before performing any academic
work. The penalties for academic dishonesty are severe and ignorance
is not an acceptable defense.
Assignments: 30% (Programs, homeworks, attendance, paper presentation)
Midterm Examination: 20%
Final Examination: 20%
Term Project: 30% (includes term paper and presentation)
Students may work on their term projects in groups of up to
three students each. The above distribution is only
tentative and may change later. The instructor will announce any
Assignment Submission Policy
Assignments must be turned in by the assigned deadline. Late
assignments will not be accepted. Rare exceptions may be made by the
instructor only under extenuating circumstances and in accordance with
the university policies.
A variety of materials will be made available on the ML Class
http://www.cs.uga.edu/~khaled/MLcourse/, including handouts,
lecture notes and assignments. Announcements may be posted between
class meetings. You are responsible for being aware of whatever
information is posted there.
Copies of some of Dr. Rasheed's lecture notes will be
available at the bottom of the class home page. Not all the lectures
will have electronic notes though and the students should be prepared
to take notes inside the lecture at any time.
Textbook in Bookstore
"Machine Learning", Tom Mitchell. McGraw-Hill, 1997. (Required.)
"Data Mining: Practical Machine Learning Tools and Techniques
(2nd edition)", Ian Witten & Eibe Frank. Morgan Kaufmann, 2005.
"Evolutionary Computation : Towards a New Philosophy of Machine
Intelligence", David Fogel. IEEE press, 1999.