In unsupervised learning, there is no target. Only input / features are given. The goal is to learn the data distribution

Examples of unsupervised learning: Finding the principal component of DNA data (dimensionality reduction), movie recommendation (matrix estimation), analyzing topics in documents (feature extraction: topic model), generating fake faces of celebrities (feature extraction: deep generative model)