Using the passage below, in Machine Learning, what is unsupervised learning and is it different from supervised learning?

Unsupervised learning is a type of algorithm that learns patterns from untagged data. The goal is that through mimicry, which is an important mode of learning in people, the machine is forced to build a concise representation of its world and then generate imaginative content from it.

In contrast to supervised learning where data is tagged by an expert, e.g. tagged as a "ball" or "fish", unsupervised methods exhibit self-organization that captures patterns as probability densities[1] or a combination of neural feature preferences encoded in the machine's weights and activations. The other levels in the supervision spectrum are reinforcement learning where the machine is given only a numerical performance score as guidance, and semi-supervised learning where a small portion of the data is tagged.

Unsupervised learning can be understood in contrast to supervised learning. The latter requires a data set tagged by an expert to train the machine learning model. The former learns patterns directly from the (unlabeled or untagged) data.