Unsupervised Learning

ELI5 (Explain like I'm 5)

Think of a child given a big pile of toys, but without telling him what they are. 

The child starts start grouping them on their own: cars in one pile, teddy bears in another, blocks in a third, based on how they look or feel similar.

That’s unsupervised learning.

In the same way, the AI model looks at data and figures out patterns by itself, without being told what the right answer is. The system just tries to make sense of what it sees.

How Unsupervised Learning Works

Unlike supervised learning, where an AI is given labeled data, unsupervised learning deals with unstructured data. The goal is not to predict a specific output, but to discover the underlying patterns in the data. This is done via 2 techniques.

1. Clustering: This is the most common technique. The algorithm groups data points that are similar to each other. Common use cases include customer segmentation, identifying shopping patterns, fraud detection in banking transactions (unusually high fund transfer) etc. 

2. Dimensionality reduction: Sometimes data is too complex (e.g, too many columns in a spreadsheet). Unsupervised learning can simplify the data and show only the information that's important for decision making. For example, when a fitness tracker turns hundreds of raw sensor readings into a single activity score. 

Unsupervised learning is often used in the early stages of data exploration, before teams know what they’re looking for.

Why unsupervised learning matters in AI

Unsupervised learning is valuable because it:

• Works without labeled data
• Scales well to large datasets
• Helps uncover insights humans may not anticipate, for example in crime investigation.

However, it also has limits. Results can be harder to interpret, and evaluation is subjective. For this reason, unsupervised learning is frequently used alongside supervised learning rather than on its own.