CS 782 Syllabus

                                  Spring 2005
                                    De Jong

Class Hours: Wednesday, 4:30 - 7:10pm

Class Room: Room 133 Innovation Hall

Office Hours: Wednesday, 3-4pm

Text: Machine Learning, T. Mitchell, McGraw Hill, 1997

Additional Reading 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 concept formation strategies, performance enhancement and skill acquisition methods, and discovery systems. Techniques to be analyzed will include symbolics methods, neural networks, and genetic algorithms. Open issues in machine learning will discussed in the context of suitable thesis topics.

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