Machine learning (nonfiction): Difference between revisions
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'''Machine learning''' is a field of [[Computer science (nonfiction)|computer science]] that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. | '''Machine learning''' is a field of [[Computer science (nonfiction)|computer science]] that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed. | ||
The name machine learning was coined in 1959 by Arthur Samuel. 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 overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision. | The name machine learning was coined in 1959 by [[Arthur Samuel (nonfiction)|Arthur Samuel]]. 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 overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit [[Algorithm (nonfiction)|algorithms]] with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision. | ||
Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. | Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. | ||
Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data. | Within the field of data analytics, machine learning is a method used to devise complex models and [[Algorithm (nonfiction)|algorithms]] that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data. | ||
== Fiction cross-reference == | == Fiction cross-reference == | ||
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* [[Gnomon algorithm]] | * [[Gnomon algorithm]] | ||
* [[Gnomon Chronicles]] | * [[Gnomon Chronicles]] | ||
* [[It's the machine learning, oxymoron]] | |||
== Nonfiction cross-reference == | == Nonfiction cross-reference == | ||
* [[Algorithm (nonfiction)]] | |||
* [[Artificial intelligence (nonfiction)]] | * [[Artificial intelligence (nonfiction)]] | ||
* [[Computer science (nonfiction)]] | * [[Computer science (nonfiction)]] | ||
* [[Arthur Samuel (nonfiction)]] | |||
External links | == External links == | ||
* [https://en.wikipedia.org/wiki/Machine_learning Machine learning] @ Wikipedia | * [https://en.wikipedia.org/wiki/Machine_learning Machine learning] @ Wikipedia | ||
* [http://t2i.cvalenzuelab.com/ Text to image generator] | |||
* [https://www.youtube.com/watch?v=cn9PEDX_qLk Tiny Machine Learning on the Edge with TensorFlow Lite Running on SAMD51] @ YouTube | |||
[[Category:Nonfiction (nonfiction)]] | [[Category:Nonfiction (nonfiction)]] | ||
[[Category:Artificial intelligence (nonfiction)]] | [[Category:Artificial intelligence (nonfiction)]] | ||
[[Category:Computer science (nonfiction)]] | [[Category:Computer science (nonfiction)]] | ||
Latest revision as of 02:48, 2 April 2021
Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.
The name machine learning was coined in 1959 by Arthur Samuel. 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 overcome following strictly static program instructions by making data-driven predictions or decisions, through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms with good performance is difficult or infeasible; example applications include email filtering, detection of network intruders, and computer vision.
Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.
Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.
Fiction cross-reference
Nonfiction cross-reference
- Algorithm (nonfiction)
- Artificial intelligence (nonfiction)
- Computer science (nonfiction)
- Arthur Samuel (nonfiction)