What is Control theory applied to machine learning?

Control theory applied to machine learning is like teaching a robot how to balance on a skateboard by watching it wobble and fall.

Imagine you're riding a bicycle, if you start leaning too far to one side, you know you need to turn the handlebars to keep from falling. That’s control theory in action: it's about making sure something stays stable or reaches a goal even when things get bumpy.

In machine learning, we use control theory to help computers learn and adjust on the go, just like how you learn to balance on a bike.

Think of it as teaching a toy car to drive itself around a track. The car might go off course sometimes, maybe it hits a bump or gets distracted by a shiny object. But with control theory, it can correct its path and keep going, just like you adjust your balance when riding.

Control theory helps computers get better over time, even when they make mistakes. It’s like having a friend who tells you, “Hey, you’re leaning too much, turn the handlebars!” That friend is the control system, helping you stay balanced and reach your goal, just like how computers learn from their errors and improve with every try.