Let’s start by telling the truth: machines don’t learn. What a typical “learning machine” does, is finding a mathematical formula, which, when applied to a collection of inputs (called “training data”), produces the desired outputs. This mathematical formula also generates the correct outputs for most other inputs (distinct from the training data) on the condition that those inputs come from the same or a similar statistical distribution as the one the training data was drawn from.
So why the name “machine learning” then? The reason, as is often the case, is marketing: Arthur Samuel,（IBM 的 Arthur Samuel（被誉为“机器学习之父”）） an American pioneer in the field of computer gaming and artificial intelligence,coined the term in 1959 while at IBM. Similarly to how in the 2010s IBM tried to market the term “cognitive computing” to stand out from competition, in the 1960s, IBM used the new cool term “machine learning” to attract both clients and talented employees.
