Unsupervised Learning

Unsupervised learning refers to a class of machine learning problems where the model is tasked with finding patterns or relationships in data without explicit guidance or labeled examples. Unlike in supervised learning problems, there are no targets \(y_i\), and datasets consist of just feature vectors: \(\mathcal D = \{\boldsymbol x_i\}_{i=1}^n\).

Here are some common types of problems associated with unsupervised learning:

1. Dimensionality Reduction: reducing the number of features or variables in a dataset while preserving essential information.

2. Clustering: grouping similar data points together without prior knowledge of class labels.

3. Anomaly Detection: identifying rare or unusual instances in a dataset that deviate from the norm.

4. Association Rule Learning: discovering interesting relationships or associations between variables in a dataset.

5. Topic Modeling: identifying topics or themes present in a collection of documents.