Machine learning is a subfield of computer science that 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 operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.
Machine learning is closely related to computational statistics; a discipline that aims at the design of algorithms for implementing statistical methods on computers.
It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field.
Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible.
Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision.
Machine learning is sometimes conflated with data mining, although that focuses more on exploratory data analysis.
Machine learning and pattern recognition "can be viewed as two facets of the same field."
When employed in industrial contexts, machine learning methods may be referred to as predictive analytics or predictive modelling.