How machines learn
A formal definition of machine learning, attributed to computer scientist Tom M. Mitchell, states that a machine learns whenever it utilizes its experience such that its performance improves on similar experiences in the future. Although this definition makes sense intuitively, it completely ignores the process of exactly how experience is translated into future action—and, of course, learning is always easier said than done!
Where human brains are naturally capable of learning from birth, the conditions necessary for computers to learn must be made explicit by the programmer hoping to utilize machine learning methods. For this reason, although it is not strictly necessary to understand the theoretical basis for learning, having a strong theoretical foundation helps the practitioner to understand, distinguish, and implement machine learning algorithms.
As you relate machine learning to human learning, you may find yourself examining your own...
