An epsilon-net is a concept from computational geometry and machine learning that helps in approximating a set of points in a given space. It consists of a subset of points that can effectively represent the larger set within a specified distance, denoted by epsilon (ε). This means that for any point in the original set, there is at least one point in the epsilon-net that is within the distance ε. Epsilon-nets are useful in various applications, such as data analysis, machine learning, and computer graphics. They allow for efficient algorithms to make decisions or predictions based on a smaller, manageable set of representative points, reducing computational complexity while maintaining accuracy.