Resource-Aware Machine Learning in the IoT Era

This lecture covers the main machine learning challenge in the IoT era, focusing on resource-constrained IoT nodes and tiny processors like MSP430F2013. It discusses complex learning algorithms, power breakdown analysis, sleep apnea, and artery-related issues. The lecture also delves into resource-aware classification, featuring low and high-quality complexity classifiers, and event-driven classification inspired by brain efficiency. Additionally, it explores datasets for training, validation, and testing, highlighting the increase in battery lifetime without significant machine-learning performance loss.