Interpretable Machine Learning in Applications

Catalog description

Introduces core machine learning (ML) algorithms with applications. Emphasizes interpretable ML methods and metrics for assessing fairness, accountability and transparency in data, models, and results. Addresses ethical considerations - justice, responsibility and trust. Covers research methodologies including literature review, proposal writing, experimental design, presentation and final reporting. Prerequisite: either CSS 343, CSS 502, CSS 549, or equivalent (advanced algorithms). Offered: S.