CSCI/ARTI 8950 Machine Learning
Instructor: Khaled Rasheed
Spring 2003

Text:

Machine Learning", Tom Mitchell. McGraw-Hill, 1997. (Required.)

Additional Books:

Data Mining", Ian Witten & Eibe Frank. Morgan Kaufmann, 2000.
Evolutionary Computation : Towards a New Philosophy of Machine Intelligence", David Fogel. IEEE press, 1999.

Web Resources:

David Aha's Machine Learning Web page
University of California at Irvine ML repository
Austrian Research Institute online ML resources page

Objectives:

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.

Recommended Background:

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, medical diagnosis, fraud detection, pattern recognition and/or other contemporary applications.

Expected Work:

Reading; assignments (may include programming and/or running existing programs); midterm; 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 students are responsible for maintaining the highest standards of honesty and integrity in every phase of their academic careers. The penalties for academic dishonesty are severe and ignorance is not an acceptable defense.

Grading Policy:

• Assignments: 25% (Programs, homewrks, paper presentation)
• Midterm Examination: 25%
• Final Examination: 25%
• Term Project: 25% (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 changes.

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.

Course Home-page:

A variety of materials will be made available on the ML Class Home-page at 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.

Lecture Notes:

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.