Unsupervised Learning

• implicit learning
• indirect feedback
• self-organizing (no labeled data)

Clustering

Clustering Identify similar samples and assign them with a label.

• k-means Clustering
• Gaussian Mixture Expectation Maximization
• Spectral Clustering
• Clustering mit Kruskal

Dimensionality Reduction

Curse of Dimensionality → grows exponentaly. Typical approaches:

• Feature Selection
• Feature Extraction (combine mutliple features into one) → Feature Engineering

For example:

• Principle Component Analysis