Portal:Machine learning
Machine learning
Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions.
Machine learning can be considered a subfield of computer science and statistics. It has strong ties to artificial intelligence and 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, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), 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."
Selected article
The Journal of Machine Learning Research (JMLR) is a scientific journal focusing on machine learning. It was founded in 2000 as an open-access alternative to Springer's journal Machine Learning. In 2001, forty editors of Machine Learning resigned in order to support JMLR, saying that in the era of the internet, it was detrimental for researchers to continue publishing their papers in expensive journals with pay-access archives. Instead, they wrote, they supported the model of JMLR, in which authors retained copyright over their papers and archives were freely available on the internet.
Selected biography
Michael Irwin Jordan (born 1956) is an American scientist, Professor at the University of California, Berkeley and leading researcher in machine learning and artificial intelligence. He has worked on recurrent neural networks, Bayesian networks, and variational methods, and co-invented latent Dirichlet allocation.
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Credit: User:Alisneaky
The effect of the kernel trick in a classifier. On the left, a non-linear decision boundary has been learned by a "kernelized" classifier. This simulates the effect of a feature map φ, that transforms the problem space into one where the decision boundary is linear (right).
Did you know?
- ... that the kernel perceptron was the first learning algorithm to employ the kernel trick, already in 1964?
- ... that AltaVista was the first web search engine to employ machine-learned ranking of its search results?
- ... that the group method of data handling, invented in the USSR, was one of the first algorithms capable of training deep neural networks (ca. 1971)?
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