This short in-person professional education course provides a comprehensive introduction to AI and machine learning tailored for power engineering applications, with an emphasis on sustainable computing practices. As the electricity industry transforms into a flat, active, and cyber-physical system, this course bridges domains to address challenges and opportunities, advancing smarter grids and sustainable AI systems. 

Topics include leveraging multi-scale data from synchrophasors, smart meters, weather, and electricity markets, exploring the operational carbon footprint of AI computing, optimizing datacenter networks for energy efficiency, and integrating environmentally responsible AI solutions. This training introduces the foundational concepts of high-dimensional spaces, data analytics, and sustainable computing practices necessary to model and operate modern power systems and datacenter networks. 

Participants will gain hands-on experience with tools for statistical time series analysis, dimensionality reduction, and energy-efficient AI solutions. We will explore the differences between first-principle models, data-driven models, and sustainable AI strategies in real-time operations, with discussions and computer-based simulation projects. These activities will help participants integrate data-driven and physics-based reasoning while considering the environmental impact of AI and computing infrastructures.