Machine learning

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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.[3]

The idea came from work in artificial intelligence.[4] Machine learning explores the study and construction of algorithms which can learn and make predictions on data.[5] Such algorithms follow programmed instructions, but can also make predictions or decisions based on data.[6]: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,[7] optical character recognition (OCR),[8] search engines and computer vision.

Using machine learning has risks. Some algorithms create a final model which is a black box.[9] Models have been criticized for biases in hiring,[10] criminal justice,[11] and recognizing faces.[12]

References[change | change source]

  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. "Machine Learning | Data Basecamp". 2021-11-26. Retrieved 2022-08-14.
  4. "Machine learning | artificial intelligence | Britannica".
  5. Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning. 30: 271–274. doi:10.1023/A:1007411609915. S2CID 36227423.
  6. Christopher Bishop 1995. Neural networks for pattern recognition. Oxford University Press. ISBN 0-19-853864-2
  7. "TechCrunch".
  8. Wernick et al 2010. Machine learning in medical imaging, IEEE Signal Processing Society|IEEE Signal Processing Magazine. 27, 4, 25-38.
  9. "Government aims to make its 'black box' algorithms more transparent". Sky News. Retrieved 2021-12-02.
  10. "Amazon scraps secret AI recruiting tool that showed bias against women". Reuters. 2018-10-10. Retrieved 2021-12-02.
  11. Mattu, Jeff Larson,Julia Angwin,Lauren Kirchner,Surya. "How We Analyzed the COMPAS Recidivism Algorithm". ProPublica. Retrieved 2021-12-02.
  12. "The Problem of Bias in Facial Recognition". www.csis.org. Retrieved 2021-12-02.