What is the difference between statistics and machine learning?

The study of data-driven intelligent systems is known as machine learning. That is all there is to it.
Data is the essential term here.

Naturally, denying computer scientists access to centuries’ worth of statistics would be ridiculous.
Machine learners have been pushing the envelope of learning speeds and statistical model sizes, even though it is exceedingly challenging to locate an idea initially published in a statistical journal without the modern twists of ML.

On the other hand, statisticians have struggled to keep up with ML breakthroughs. So it isn’t entirely one-sided.
Problems raised in machine learning continue to motivate the discipline of mathematical optimization.
Perhaps a better description would be “ML is the marriage of statistics and operational research.”

What about the sophisticated data structures and algorithms that enable us to tackle issues with billions of variables?
Traditional computer science deserves credit for this.
For graph theory, mathematicians are also needed.
Also in favor of connectionism are neuroscientists.

It isn’t sensible to play the attribution game and characterize afield as the sum of its intellectual forerunners.