The intent of this course is to present a broad introduction to Machine
Learning research in Artificial Intelligence including discussions of each of
the major approaches.  These include: learning from examples, learning from
observation and discovery, explanation-based learning, neural-net learning,
analogy, case-based reasoning, and learnability theory. The primary focus of
the course will be on understanding the underlying algorithms used in systems
representing each approach.
