Energy-Based Models (EBMs) offer a powerful framework for machine learning by assigning a scalar energy value to configurations of variables, where the aim is to find configurations with low energy to represent probable states. They are particularly adept at unsupervised learning tasks where the goal is to capture complex data distributions and are closely linked to concepts in statistical physics.