Embedded Machine Learning Systems

Catalog description

This course introduces machine learning with a focus on applications on embedded systems. The course explores problems and techniques in supervised, unsupervised and reinforcement learning with an emphasis on problems encountered in embedded contexts. A variety of classical and deep learning models, model training by optimization, and running models on embedded systems are covered. Students will run machine learning models on embedded systems equipped with GPUs in the lab setting. Applied AI Integration: This course meets or exceeds MSOE’s Applied AI Integration guidelines for substantial coverage of Foundational Knowledge, Practical Application, and Critical Evaluation.