Representation learning studies how to learn useful features directly from data rather than hand-engineering them.
This topic is central when the main challenge is not just prediction, but discovering a latent space that makes structure, similarity, and downstream tasks easier to model.
Typical examples include embeddings, autoencoders, self-supervised learning, and latent-variable models.