# Unsupervised Learning

In this kind of learning, we do not have labeled training data.

The most common unsupervised problem is **clustering**. There are also other unsupervised problems such as dimensionality reduction.

Some applications of clustering include:

* color quantization (16 million colors to 256 colors)
* directed marketing (different ads for different groups of people)
* pre-processing stage in supervised learning (in certain cases where it is clear that the examples are coming from well-defined groups)