Professor: Andrea Danyluk email: andrea "at" cs "dot" williams "dot" edu phone: x2178 Office: TCL 305 Office Hours: Monday 9PM-11PM, Tuesday 9PM-11PM, Thursday 1:30PM-2:30PM. You can find me in my office most Sundays. My schedule is pretty flexible on Mondays and Fridays. Tuesdays and Wednesdays are a bit more full with various meetings, but feel free to try then as well.

Machine Learning is an area within Artificial Intelligence that has as its aim the development and analysis of algorithms that are meant to automatically improve a system's performance. Automatic improvement might include: (1) learning to perform a new task; (2) learning to perform a task more efficiently or effectively; or (3) learning and organizing new facts that can be used by a system that relies upon such knowledge.

Machine learning algorithms have influenced work in areas other than Artificial Intelligence itself. For example, some of the algorithms developed have contributed to the study of human learning in cognitive science and cognitive psychology. Recent practical applications of machine learning algorithms have included data mining for discovery of new information from scientific data, for fraud detection, and for market analysis.

There are many ways to categorize machine learning algorithms. One is based on the amount and type of background information provided to the algorithm. This course will introduce specific instances of two categories of learning algorithms - supervised algorithms and unsupervised algorithms. It will also introduce methods for evaluating the performance of machine learning algorithms. Finally, it will introduce topics in computational learning theory.

Work in the course will involve reading, solving problems, running and evaluating learning algorithms, and implementing some as well.