The term “machine learning” has started to be thrown around everywhere. However, I just don’t get excited about it, myself.
Machine learning is literally just another name for curve-fitting. That’s all it is. Curve fitting has always been very useful for establishing patterns where the underlying mechanism is unknown, and I’m glad that we have automated the curve-fitting process, and developed a number of techniques for it. But let’s be real. The hype is ridiculous. 99% or greater of what is passed off as “machine learning” is really just glorified curve fitting.
If you see someone say something about “machine learning” on the news, pretty much every time they just mean that someone ran a curve-fitting system on data for which we don’t have underlying physical means of projecting.
Now, the funny thing is, the people doing machine learning have generally convinced themselves that this is not what they are doing. But if you look at the components, it is just a stochastic curve-fitting technique which can make use of non-linear components.
Again, it is useful in its context, but we’ve been doing curve-fitting for hundreds of years, and it wasn’t until the last decade that people have been writing articles about it like it was a brand-new idea.