Portal:Machine learning
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
Lua error in package.lua at line 80: module 'Module:Box-header/colours' not found. 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.
Lua error in package.lua at line 80: module 'Module:Box-header/colours' not found. 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.
Template:/box-header Portal:Machine learning/News Template:/box-footer
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Credit: User:Fyedernoggersnodden
An Elman network, one of the simplest types of recurrent neural networks. The network has an input layer, a hidden layer and an output layer. The hidden layer is connected to a context layer (bottom) that remembers its activation at the previous observation, giving the network a memory and making it capable of processing sequences (e.g., sequences of words or of phonemes).
- ... 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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