Landmark 1 stop to get here · leads to 6

Unsupervised Learning

Learning from unlabeled data to discover hidden patterns, structures, or relationships without explicit target outputs.

Your route here

1 stop · basics first
  1. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  2. Unsupervised Learning · you are here ✓ understood

Picture it

Supervised

  • Data comes with labels (x, y)
  • Learns to predict y from x
  • e.g. classification, regression

Unsupervised

  • Only inputs x, no labels
  • Finds structure in the data itself
  • e.g. clustering, dimensionality reduction
Notice the missing column of answers: unsupervised learning has no target to match, so it looks for groups and patterns instead.

Where it sits

Before this

Machine Learning
Unsupervised Learning

Leads to

Self-Supervised Learning Anomaly Detection Autoencoder Clustering Dimensionality Reduction Semi-Supervised Learning

Explore nearby

Foundations Supervised Learning Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen. Foundations Clustering An unsupervised learning technique that groups similar data points together based on their features or characteristics. Foundations Dimensionality Reduction Techniques to reduce the number of input features while preserving important information (PCA, t-SNE, autoencoders). Foundations Anomaly Detection Identifying unusual patterns or outliers in data that don't conform to expected behavior, used for fraud detection and monitoring. Training Self-Supervised Learning Learning representations from unlabeled data by creating supervised tasks from the data itself (masked prediction, contrastive learning).