Publication Date

Spring 5-2024


School of Business; School of Visual and Performing Arts


Computer Science


The purpose of this thesis is to examine the usefulness of machine learning in the art world with a simple model. This model is an image classifier designed to identify the type of classical composition present in landscape paintings. After several tests and various modifications, the image classifier achieved around 50% accuracy on images within its dataset. For images outside of the dataset the model’s results were 65% accuracy on an easier image and 15% accuracy on a more difficult image. This renders the model impractical for educational purposes and raises questions as to how current machine learning techniques in the art world could be improved by more accurate aesthetic evaluation.