Machine learning

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 artificial neural 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]

Steps in the Machine Learning Process

1. Data Collection and Preparation: Start by gathering data and cleaning it up to ensure it’s accurate and ready to use.
2. Choosing a Model: Select the right algorithm or model that best fits the problem you’re trying to solve.
3. Training the Model: Input the data into the model and adjust it to enhance its performance.
4. Evaluating the Model: Test the model’s effectiveness by using metrics like accuracy and precision.
5. Deployment and Monitoring: Implement the model in a real-world setting and continuously monitor its performance over time.

1. ↑ John McCarthy & Edward Feigenbaum 1990. In Memoriam Arthur Samuel: pioneer in machine learning. AI Magazine. AAAI. 11 (3).[1] Archived 2018-01-22 at the Wayback Machine
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.{{cite web}}: CS1 maint: multiple names: authors list (link)
12. ↑ "The Problem of Bias in Facial Recognition". www.csis.org. Retrieved 2021-12-02.