Elect Engr & Computer Science - Embedded Machine Learning

Catalog description

As AI moves to the edge, the ability to deploy machine learning algorithms on embedded systems is becoming increasingly valuable. This introductory course explores the development and deployment of machine learning models on resource-constrained embedded systems. Students will learn core machine learning principles, model optimization techniques (e.g., quantization), and deployment strategies for microcontrollers and edge devices. Through hands-on assignments and projects, they will gain practical skills and a deeper understanding of the challenges and opportunities of deploying machine learning models on embedded hardware. Prerequisite: EECS 388, or consent of instructor.