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.
The Class: Format: tutorial
Requirements/Evaluation: evaluation will be based on presentations, problem sets, programming exercises, empirical analyses of algorithms, critical analysis of current literature, and a final exam
Extra Info: may not be taken on a pass/fail basis
Prerequisites: CSCI 136 and MATH 200 (formerly 251); CSCI 256 is recommended but not required
Enrollment Preferences: Computer Science majors
Distributions: Division III Quantative/Formal Reasoning
Attributes: COGS Interdepartmental Electives