Artificial neural networks in high-energy physics

Artificial neural networks are the machine learning technique best known in the high-energy physics community. Introduced in the field in 1988, followed by a decade of tests and applications received with reticence by the community, they became a common tool in high-energy physics data analysis. Important physics results have been extracted using this method in the last decade. This lecture makes an introduction of the topic discussing various types of artificial neural networks, some of them commonly used in high-energy physics, other not explored yet. Examples of applications in high-energy physics are also briefly discuss with the intention of illustrating types of problems which can be addressed by this technique rather than providing a review of such applications.