A hands-on introduction to computational approaches for learning from data. The course focuses on applying machine learning methods to real world data and the issues that come with it, including data cleaning and preparation and model selection and evaluation. Topics include linear models for supervised learning, preprocessing, feature selection, ensembles, clustering, and neural networks. Prerequisite: Grade of C or better in COMP 152. Offered in alternate years. (One course).
- Teacher: Robert Utterback