This course is designed for advanced undergraduate students who have basic knowledge about artificial intelligence. The primary objective of this course is to introduce and study some basic principles, techniques, and applications of a variety of learning models.

Some of the areas we will cover in this course include concept learning, inductive inference of decision trees, artificial neural networks, genetic algorithms, bayesian learning, computational learning, and reinforcement learning.

The emphasis of the course is on teaching the fundamentals, and not on providing a mastery of specific commercially available software tools or programming environments. Written and programming assignments are used to help clarify basic concepts. The student will learn research skills applicable to other fields of computer science. Each student will be expected to understand and present a selected current machine learning research paper and complete a term project.