Hello again, everyone!

I'm Anna, the AI professor.

Last time we explored the world of supervised learning, where machines learn from labeled examples to make predictions.

Today we'll examine a different approach: unsupervised learning. In unsupervised learning, the machine discovers patterns in unlabeled data.

AI systems that use the unsupervised learning approach are widely applied across various industries. Let's take a look at some examples.

There are AI systems that analyze customer behavior and group customers based on their purchasing habits. This enables targeted marketing strategies and helps companies spot trends early and make informed decisions.

These AI systems typically use unsupervised learning to segment customers into distinct groups.

Unsupervised learning also underpins many other common AI systems in our society, for example:

AI systems that detect fraudulent transactions in financial systems by identifying anomalies in transaction patterns.
AI systems that automatically organize images and videos by grouping them according to visual similarities.

But how do machines learn to recognize patterns in unlabeled data and solve real-world problems?

Let's dive deeper and learn more about the unsupervised learning approach!