Machine Learning.

Catalog description

This course introduces concepts of machine learning and statistical pattern recognition at the graduate level. The course covers topics such as linear and logistic regression, classification, clustering, model validation, support vector machines, neural networks, and decision trees. Data wrangling methods and dimensionality reduction are also examined. Prerequisites: Algorithms Core Skill and graduate students in the School of Engineering and Computer Science or instructor approval.