Unsupervised Learning

Lesson goals

• Clustering
• Dimensionality reduction
• Evaluation challenges

Core idea

Discovering clusters and hidden structure without provided answers.

1. Clustering: understand it in context, identify inputs and outputs, and define how the result will be verified.

2. Dimensionality reduction: understand it in context, identify inputs and outputs, and define how the result will be verified.

3. Evaluation challenges: understand it in context, identify inputs and outputs, and define how the result will be verified.

Practical example

Consider a real application of “Unsupervised Learning.” Start small, identify available data, and compare the system with a clear baseline before expanding it.

Check your understanding

1. Explain “Unsupervised Learning” in your own words.
2. Give one appropriate and one misleading application.
3. What risk or limitation should be monitored?