The article explores the PAC learning model in computational learning theory, which helps compare algorithms' performance and understand sample and computational complexity of concept classes. The researchers introduce and analyze the basic PAC learning model, provide a method to determine if a concept class is PAC learnable, and extend the model to account for errors. They also discuss other learning models in computational learning theory.