Machine Learning

Catalog description

Foundational Theory, Models, and methods of supervised machine learning, including VC dimension, validation, linear models, artificial neural networks, and support vector machines. Various learning algorithms will be implemented and tested, such as perceptron learning, linear regression, and gradient descent. Prerequisite: grade of ""C"" or better in MATH 115 and COSC 300.