Portal:Machine learning

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Template:/box-header 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." Template:/box-footer

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Overfitting occurs when a statistical model describes random error or noise instead of the underlying relationship. Overfitting generally occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. A model that has been overfit will generally have poor predictive performance, as it can exaggerate minor fluctuations in the data.

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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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Kernel Machine.png
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).





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