CSCI 374 - Machine Learning

Institution:

Williams College

Subject:

Computer Science

Description:

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.

Credits:

3.00

Credit Hours:

Prerequisites:

Computer Science 136 and Mathematics 251; Computer Science 256 is recommended but not required

Corequisites:

Exclusions:

Level:

Instructional Type:

Other

Notes:

Additional Information:

Historical Version(s):

Institution Website:

www.williams.edu

Phone Number:

(413) 597-3131

Regional Accreditation:

New England Association of Schools and Colleges

Calendar System:

Four-one-four plan

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