Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives “computers the ability to learn without being explicitly programmed.” Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs.

“L’apprentissage par machine est le sous-domaine de l’informatique qui, selon Arthur Samuel en 1959, donne à «l’informatique la possibilité d’apprendre sans être explicitement programmée». Évolué à partir de l’étude de la reconnaissance de formes et de la théorie de l’apprentissage informatique dans l’intelligence artificielle, l’apprentissage par machine explore l’étude et la construction d’algorithmes qui peuvent apprendre et faire des prédictions sur les données. Ces algorithmes suppriment les instructions de programme strictement statiques en faisant des prédictions ou des décisions axées sur les données , En construisant un modèle à partir d’échantillons d’intrants”

Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.