This tutorial examines the design, implementation, and analysis of machine learning algorithms. Machine Learning is a branch of Artificial Intelligence that aims to develop 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 examples of supervised learning algorithms (including decision tree learning, support vector machines, and neural networks), unsupervised learning algorithms (including k-means and expectation maximization), and possibly 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.
Grading: no pass/fail option,
no fifth course option
presentations, problem sets, programming exercises, empirical analyses of algorithms, critical analysis of current literature
CSCI 136 and CSCI 256 or permission of instructor
Computer Science majors
COGS Interdepartmental Electives