Unsupervised Machine Learning

My handbook

Michael Foley

2021-10-03

Intro

Machine learning (ML) develops algorithms to identify patterns in data (unsupervised ML) or make predictions and inferences (supervised ML).

Unsupervised machine learning searches for structure in unlabeled data (data without a response variable). The goal of unsupervised learning is clustering into homogenous subgroups, and dimensionality reduction. Examples of cluster analysis are k-means clustering, hierarchical cluster analysis (HCA), and PCA (others here).