EE292D

This is a project-based class where students will learn how to develop machine learning models for execution in resource constrained environments such as embedded systems. The primary target is embedded devices such as Arduino, Raspberry PI, Jetson, or Edge TPUs... The class is broken into lectures/readings, labs/assignments, and a final project. Throughout this class, students will learn techniques such as model quantization, knowledge distillation and hybrid (embedded/cloud inferencing), which are instrumental for building efficient machine learning models that can run on power or resource-constrained devices. This class differs from other machine learning classes offered at Stanford due to the focus of applying these models for applications that require running on embedded hardware or other resource-constrained environments... Course logistics. Introduction on machine learning and its standard training and deployment practice. Introduction to TFLite. First embedded project!