5.1.2. Unsupervised learning

In unsupervised learning, the task is to find patterns or meaning in untagged data such as classifying similar data together without actually knowing what those classes may represent (clustering) or we take data in some low level/uncompressed representation and learn a high level/compressed representation with minimum information loss or we have a lot of data which mostly subscribes to a particular pattern and we would like to detect the outliers (anomaly detection).

K-means clustering, autoencoders (NN based) and principal component analysis are a few algorithms used for unsupervised learning tasks.
