Representation learning is a subfield of machine learning focused on developing methods for automatically discovering effective transformations of raw data into representations that facilitate downstream tasks. It investigates techniques for learning hierarchical feature representations that capture underlying explanatory factors and variations, aiming to improve model performance by reducing reliance on manual feature engineering.