Note: The following provides a suggested course description,
objectives, and an outline. These may be modified pending discussion with
the Faculty Chairs, proposing faculty, and other curriculum
reviewers.
Course Description: This course provides intermediate
background in intelligent systems for graduate and advanced undergraduate
students. Three topics will be discussed: data mining and machine learning
(including analytical and multistrategy learning, knowledge-driven
learning, and inductive learning), pattern recognition using artificial
neural networks and Bayesian networks, and genetic algorithms and
evolutionary systems for KDD.
Course Objectives:
Applications to practical design and development of intelligent
systems will be emphasized, leading to class projects on current research
topics. Students will review and critique current research papers and
learn fundamentals of data mining algorithms and implementations through
research discussion and written and programming assignments.
