CSCI 374T Machine Learning (Not offered 2006-2007; to be offered 2007-2008) (Q)
This tutorial examines the design, implementation, and analysis of machine
learning algorithms. Machine Learning is a branch of Artificial Intelligence that
has as its aim the development of algorithms that will improve a system's performance. Improvement might involve acquiring new factual knowledge from
data, learning to perform a new task, or learning to perform an old task more
efficiently or effectively. This tutorial will cover instances of three general categories of algorithms: supervised learning algorithms (including decision tree
learning, support vector machines, and neural networks), unsupervised learning
algorithms (including k-means and expectation maximization), and reinforcement learning algorithms (such as Q learning and temporal difference learning).
It will also introduce methods for the evaluation of learning algorithms, as well
as topics in computational learning theory.
Format: tutorial. Evaluation will be based on presentations, problem sets, short
programming exercises, empirical analyses of algorithms, and two exams.
Prerequisites: Computer Science 136 and Mathematics 251. Computer Science
256 is recommended but not required. Enrollment limit: 10 (expected: 10). Preference given to Computer Science majors.