CS 782 Syllabus

Fall 2008

De Jong

Class Hours:  Thursday, 7:20-10:00 pm

Class Room:  Room 134 Innovation Hall

Required text:   None

Reference texts:

• Machine Learning, T. Mitchell, McGraw Hill, 1997

• Introduction to Machine Learning, E. Alpaydin, MIT Press, 2004

• Pattern Recognition and Machine Learning, C. Bishop, Springer, 2006

Additional Reference Material:

• Machine Learning, Volumes I, II, III, ... Various editors, Morgan Kaufmann Publ.
• Readings in Machine Learning, Shavlik & Dietterich (eds), Morgan Kaufmann Publ., 1990.
• Machine Learning Journal, Kluwer Publishing.
• Proceedings of the Machine Learning Conferences.

Prerequisites: CS 580 and either CS 681, or CS 687, or CS 688 or permission of the instructor.

Content: The basic principles of machine learning will be presented from a computational point of view. The material will provide good historical coverage of the important developments in the field. Topics to be covered will include classification and model formation strategies, performance enhancement and skill acquisition methods, and discovery systems. Techniques to be covered will include symbolics methods, statistical methods, and reinforcement learning methods. Both formal and empirical techniques for analyzing machine learning methods will be covered.

Exams: There will be one in-class exam.

Homework: There will be 3-4 programming assignments which will include written summaries.

Project: A class project/paper will be required and will consist of both an oral and written presentation.

Grading: The course grade will be determined approximately as follows:

                        homework:       1/3

                        project:        1/3

                        exam:           1/3