Machine Learning (ML) Subject Area
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A Machine Learning (ML) Subject Area is a subject area that focuses on machine learning tasks, machine learning algorithms, and machine learning systems.
- AKA: Field of ML.
- Context:
- It can be represented by a Machine Learning Ontology (of ML concepts and ML relations).
- It can include ML Research.
- …
- Example(s):
- Counter-Example(s):
- See: Machine Learning Discipline, Predictive Modelling, Pattern Recognition, Computational Learning Theory, Artificial Intelligence, Mathematical Optimization.
References
2015
- (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/machine_learning Retrieved:2015-6-28.
- Machine learning is a subfield of computer science[1] that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the construction and study of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. It has strong ties to mathematical optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR),[2] search engines and computer vision. Machine learning is sometimes conflated with data mining, although that focuses more on exploratory data analysis. Machine learning and pattern recognition "can be viewed as two facets of
the same field." When employed in industrial contexts, machine learning methods may be referred to as predictive analytics or predictive modelling.
- Machine learning is a subfield of computer science[1] that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the construction and study of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. It has strong ties to mathematical optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR),[2] search engines and computer vision. Machine learning is sometimes conflated with data mining, although that focuses more on exploratory data analysis. Machine learning and pattern recognition "can be viewed as two facets of
- ↑ http://www.britannica.com/EBchecked/topic/1116194/machine-learning
- ↑ Wernick, Yang, Brankov, Yourganov and Strother, Machine Learning in Medical Imaging, IEEE Signal Processing Magazine, vol. 27, no. 4, July 2010, pp. 25-38
- (Jordan & Mitchell, 2015) ⇒ Michael I. Jordan, and Tom M. Mitchell. (2015). “Machine Learning: Trends, Perspectives, and Prospects.” In: Science Journal, 349 (6245). doi:10.1126/science.aaa8415
- QUOTE: Machine learning addresses the question of how to build computers that improve automatically through experience.
2014
- Andrew Ng. (2014). “Machine Learning MOOC." Coursera
- QUOTE: Machine learning is the science of getting computers to act without being explicitly programmed.