Huembeli, PatrickArrazola, Juan MiguelKilloran, NathanMohseni, MasoudWittek, Peter2022-01-312022-01-312022-01-312022-06-0110.1007/s42484-021-00057-7https://infoscience.epfl.ch/handle/20.500.14299/185026WOS:000739948300001Energy-based models (EBMs) are experiencing a resurgence of interest in both the physics community and the machine learning community. This article provides an intuitive introduction to EBMs, without requiring any background in machine learning, connecting elementary concepts from physics with basic concepts and tools in generative models, and finally giving a perspective where current research in the field is heading. This article, in its original form, was written as an online lecture note in HTML and Javascript and contains interactive graphics. We recommend the reader to also visit the interactive version.Computer Science, Artificial IntelligenceQuantum Science & TechnologyComputer SciencePhysicsenergy-based modelsmachine learningspin glassesgibbs samplingrestricted boltzmann machinesspin-glass modelsquantumoptimizationalgorithmnetworkstext::journal::journal article::research article