Recent Advances in Machine Learning with Applications to Internet of Things - Adam McElhinney

This course covers the learning outcomes and goals of understanding recent advances in machine learning as applied to the Internet of Things (IoT). It aims to teach the differences between physics-based and data-driven approaches to IoT, challenges in applying data-driven approaches, and how machine learning can address these challenges. The course also focuses on failure prediction, prognostics, and real-case studies illustrating the application of deep learning, gradient boosting, transfer learning, and other machine learning techniques for IoT applications. The teaching method includes theoretical overviews, discussions on challenges and advancements, and a real-case study. The intended audience for this course includes individuals interested in machine learning, IoT, industrial equipment, and data analysis in the context of sensor technologies.