Description: 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 selected from 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.
Fulfills the Quantitative Reasoning requirement
Prerequisites: Computer Science 136 and Mathematics/Statistics 251. Computer Science 256 is recommended but not required.
Scheduled Offerings:
