How machine learning from data works
Many definitions of ML revolve around the automated detection of meaningful patterns in data. Two prominent examples include:
- AI pioneer Arthur Samuelson defined ML in 1959 as a subfield of computer science that gives computers the ability to learn without being explicitly programmed.
- Tom Mitchell, one of the current leaders in the field, pinned down a well-posed learning problem more specifically in 1998: a computer program learns from experience with respect to a task and a performance measure of whether the performance of the task improves with experience (Mitchell 1997).
Experience is presented to an algorithm in the form of training data. The principal difference from previous attempts of building machines that solve problems is that the rules that an algorithm uses to make decisions are learned from the data, as opposed to being programmed by humans as was the case, for example, for expert systems prominent...
