Unsupervised Learning

Level: Beginner Duration: 45 minutes Download PDF

Unsupervised Learning

Discovering patterns in data without predefined labels.

Learning Outcomes

By completing this topic, you will:

  • Explain when unsupervised learning is appropriate
  • Compare clustering, dimensionality reduction, and association rules
  • Evaluate cluster quality without ground truth
  • Apply PCA for visualization

Visual Guides

Clustering Example
Clustering Example
Dimensionality Reduction
Dimensionality Reduction
Supervised vs Unsupervised
Supervised vs Unsupervised

Prerequisites

  • ML Foundations concepts
  • Basic understanding of distance metrics
  • Familiarity with data distributions

Key Concepts

Clustering

Group similar data points together:

  • K-means for spherical clusters
  • DBSCAN for arbitrary shapes
  • Hierarchical for nested structures

Dimensionality Reduction

Compress high-dimensional data:

  • PCA preserves variance
  • t-SNE for visualization
  • UMAP for structure preservation

Association Rules

Find relationships between items:

  • Market basket analysis
  • Support, confidence, lift metrics

When to Use

Unsupervised learning is ideal when:

  • You lack labeled data
  • You want to discover natural groupings
  • You need to reduce feature dimensionality
  • You want to find hidden patterns

Common Pitfalls

  • Choosing K arbitrarily in K-means
  • Ignoring the curse of dimensionality
  • Over-interpreting cluster meanings
  • Using wrong distance metric for data type

(c) Joerg Osterrieder 2025