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
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“Above all, however, the machine has no feelings, it feels no fear and no hope ... it operates according to the pure logic of probability. For this reason I assert that the robot perceives more accurately than man.”
—Max Frisch (19111991)
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Drink deep, or taste not the Pierian spring:
There shallow draughts intoxicate the brain,
And drinking largely sobers us again.”
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