Course description: Introduction to machine learning, including computational learning theory, major approaches to machine learning, evaluation of models, and current research

Lecture: Tuesday & Thursday 3:30 - 4:45, Simrall 106

Instructor: Susan Bridges, bridges@cs.msstate.edu

• Office: Butler 312, tel. 325-7505

• Office hours: Monday 1:30 - 3:00, Wednesday 10:00 - 11:30

Textbook: Mitchell, Tom M. 1997. Machine Learning. Boston, MA: McGraw-Hill Company.

Duda, R. O., P. E. Hart, and D. G. Stork. 2001. Pattern Classification. New York, Wiley Interscience.

Han, J. and M. Kamber. Data Mining: Concepts and Techniques. San Francisco, CA: Morgan Kaufmann.

Hand, D., H. Mannila, and P. Smyth. 2001. Principles of Data Mining. Cambridge, MA: The MIT Press.

Langley, Pat. 1996. Elements of Machine Learning. San Francisco, CA: Morgan Kaufmann Publishers.

Russell, Stuart, and Peter Norvig. 2003. Artificial Intelligence: A Modern Approach, Second Edition. Upper Saddle River, New Jersey: Prentice Hall.

Weiss, S. M. and C. A. Kulikowski. 1991. Computer Systems that Learn. San Mateo, CA: Morgan Kaufmann Publishers.

Course schedule: See schedule

Grading:

• 40% exams (midterm and final--20% each)

• 25% projects/homework

• 25% term paper

• 10% class participation

Attendance: Students are expected to attend all classes. If you must be absent, you should notify the instructor ahead of time if possible. More than 3 unexcused absences will result in a reduction of one letter grade in the course. Students who are auditing the course must attend all lectures. An audit will not be awarded if a student has more than 3 unexcused absences.

Academic honesty policy: Cheating on an exam will result in an F for the course as well as possible university disciplinary measures. A student is to consult no one other than the instructor of this course for help on an exam. A student must cite any references used in answering exam questions or resources used to complete projects and term papers. Failure to properly cite references can result in a grade of 0 on the assignment. Consulting anyone other than the instructor and/or failing to cite references constitute cheating on an exam. The projects, homework, and term paper are individual endeavors. Students may consult only with the instructor for help on these assignments unless specifically told otherwise on the assignment. Cheating on the assignments will result in a grade of F for the course as well as possible university disciplinary measures. See the Computer Science Departmental Policy Regarding Professional Conduct. The University policy regarding academic honesty applies to all MSU students.

Add/drop policy: See the Mississippi State University Add/Drop Policy