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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.

References

  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. pp. 89. ISBN 978-1-118-63817-0 . https://books.google.com/books?id=Dn-Gdoh66sgC&pg=PA89#v=onepage&q&f=false. 
  3. [2]
  4. Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning 30: 271–274. http://ai.stanford.edu/~ronnyk/glossary.html. 
  5. Christopher Bishop 1995. Neural networks for pattern recognition. Oxford University Press.

    Template-specific style sheet:

    ISBN 0-19-853864-2
  6. "TechCrunch". https://techcrunch.com/2016/07/01/exploiting-machine-learning-in-cybersecurity/. 
  7. Wernick et al 2010. Machine learning in medical imaging, IEEE Signal Processing Society|IEEE Signal Processing Magazine. 27, 4, 25-38.