Actually I'm fairly ignorant of the "nuts and bolts" of computer science, although I did see in Google Scholar that the "Extreme Learning Machine" concept has been around for quite some time.
I have a general feel for computational chemistry theory, the general concept of the Kohn-Sham theorem for "normal" density functional theory, and am trying to wrap my little brain around "orbital free density functional theory" but I don't know how it all works on a computational level.
From what I gather from poking around the internet, throwing in some of your lexicon, while snowed in - and I'm not likely to be able to leave this house without many hours of digging - the concept of "machine learning" involves using a "learning set," or "training set," computing a "best fit" using (hopefully convergent) calculations, and then weighting the results to approach a better fit.
For many chemists, myself certainly included since I am running out of time on the planet and will never have the time to learn the details, the "nuts and bolts," of computational science it's all rather like ordering a meal in a fine restaurant. You don't know how the food was prepared, but you enjoy the taste anyway.
My youngest son, still in high school, is considering a career in Materials Science, and I hope he will be inspired to learn more about these important "nuts and bolts" than his old man did.
Thanks for your stimulating comment!
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