Unsupervised Learning

When Sir Isaac Newton discovered the principle of gravity, he wasn’t experimenting in a laboratory with close supervision from his professor. Rather, he was sitting alone, unsupervised, under a tree when he happened to observe an apple suddenly fall to the ground. Newton carefully observed the plummeting fruit and, through his own curiosity and creativity, formulated the laws of motion and universal gravitation.

Sometimes we learn best through processes of exploration, experimentation, and self-directed inquiry. As children, we are encouraged to play without adult guidance to develop crucial problem-solving skills, creativity, and independence of thought (and because parents don’t have unlimited time for play). Later in life, this autonomous learning can lead to significant discoveries and innovations, just like Newton and gravity. 

When computers are left to their own devices, they can be pretty creative too.

Unsupervised learning is a type of machine learning that does not require human supervision. The input data does not have labels and so the goal is for the model to identify patterns, structures, and relationships within the data without explicit instructions on what to look for. In other types of machine learning, such as supervised learning, the model is trained on a dataset which contains input-output pairs that have been labeled by a human. 

You may be thinking, why are there different types of machine learning? Well, some types of machine learning are better suited to certain tasks than others. Unsupervised learning algorithms are excellent at handling complex processing tasks, such as organizing large datasets into clusters. They are also very effective at uncovering hidden patterns in data and can identify key features that help categorize information.¹ In fact, unsupervised learning can discover patterns that may not be immediately obvious to humans.