Unsupervised Learning

What if you have data but no labels? What if you don't know what patterns to look for?

Unsupervised learning finds hidden patterns in data without being told what to look for. There's no "right answer" to learn from - the algorithm discovers structure on its own.

Supervised vs Unsupervised

Main Types of Unsupervised Learning

1. Clustering: Grouping Similar Things

Clustering finds natural groups in data. Examples that are similar get grouped together.

Real Clustering Applications

2. Dimensionality Reduction: Simplifying Data

Sometimes data has too many features. Dimensionality reduction finds the most important patterns and simplifies the data while preserving key information.

3. Association Rule Learning: Finding Relationships

Discovers interesting relationships between items in large datasets.

4. Anomaly Detection: Finding Outliers

Identifies unusual data points that don't fit the normal pattern.

When to Use Unsupervised Learning

Supervised vs Unsupervised: Summary

Aspect	Supervised	Unsupervised
Data	Labeled (has answers)	Unlabeled (no answers)
Goal	Predict outputs	Find patterns
Tasks	Regression, Classification	Clustering, Dimensionality Reduction
Evaluation	Compare to known answers	Harder to evaluate
Example	Spam detection	Customer segmentation

Key Takeaways

• Unsupervised learning finds patterns without labeled data
• Clustering groups similar data points together
• Dimensionality reduction simplifies data while preserving information
• Association rules find relationships between items
• Anomaly detection identifies unusual data points
• Use when you don't have labels or want to explore data structure

Next, we'll learn about reinforcement learning - where agents learn by trial and error!