Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the development of algorithms that take as input empirical data, such as that from sensors or databases. The algorithm is designed to (a) identify (i.e., quantify) complex relationships thought to be features of the underlying mechanism that generated the data, and (b) employ these identified patterns to make predictions based on new data. Data can be seen as instances of the possible relations between observed variables; the algorithm acts as a machine learner which studies a portion of the observed data (called examples of the data or training data) to capture characteristics of interest of the data's unknown underlying probability distribution, and employs the knowledge it has learned to make intelligent decisions based on new input data.
One fundamental difficulty is that the set of all possible behaviors given all possible inputs is (in most cases of practical interest) too large to be included in the set of observed examples. Hence the learner must generalize from the given examples in order to produce a useful output from new data inputs.
Optical character recognition, in which printed characters are recognized automatically based on previous examples, is a classic engineering example of machine learning.
Read more about Machine Learning: Definition, Generalization, Machine Learning, Knowledge Discovery in Databases (KDD) and Data Mining, Human Interaction, Algorithm Types, Theory, Applications, Software, Journals and Conferences
Famous quotes containing the words machine and/or learning:
“But it is found that the machine unmans the user. What he gains in making cloth, he loses in general power. There should be a temperance in making cloth, as well as in eating.”
—Ralph Waldo Emerson (1803–1882)
“Without our being especially conscious of the transition, the word “parent” has gradually come to be used as much as a verb as a noun. Whereas we formerly thought mainly about “being a parent,” we now find ourselves talking about learning how “to parent.” . . . It suggests that we may now be concentrating on action rather than status, on what we do rather than what or who we are.”
—Bettye M. Caldwell (20th century)