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Machine learning

branch of statistics and computer science, which studies algorithms and architectures that learn from observed facts

Machine learning gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959).[1][2] It is a subfield of computer science.

The idea came from work in artificial intelligence.[3] Machine learning explores the study and construction of algorithms which can learn and make predictions on data.[4] Such algorithms follow programmed instructions, but can also make predictions or decisions based on data.[5]:2 They build a model from sample inputs.

Machine learning is done where designing and programming explicit algorithms cannot be done. Examples include spam filtering, detection of network intruders or malicious insiders working towards a data breach,[6] optical character recognition (OCR),[7] search engines and computer vision.


  1. John McCarthy & Edward Feigenbaum 1990. In Memoriam Arthur Samuel: pioneer in machine learning. AI Magazine. AAAI. 11 (3).[1]
  2. Phil Simon (2013). Too big to ignore: the business case for big data. Wiley. p. 89. ISBN 978-1-118-63817-0.
  3. [2]
  4. Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning 30: 271–274. 
  5. Christopher Bishop 1995. Neural networks for pattern recognition. Oxford University Press. ISBN 0-19-853864-2
  6. "TechCrunch".
  7. Wernick et al 2010. Machine learning in medical imaging, IEEE Signal Processing Society|IEEE Signal Processing Magazine. 27, 4, 25-38.