Unsupervised learning is a method of machine learning where a model is fit to observations. It is distinguished from supervised learning by the fact that there is no a priori output. In unsupervised learning, a data set of input objects is gathered. Unsupervised learning then typically treats input objects as a set of random variables. A joint density model is then built for the data set. As a broad subfield of artificial intelligence, Machine learning is concerned with the development of algorithms and techniques, which allow computers to learn. At a general level, there are two types of learning: inductive, and deductive. ... Supervised learning is a machine learning technique for creating a function from training data. ... A random variable is a term used in mathematics and statistics. ...
Unsupervised learning can be used in conjunction with Bayesian inference to produce conditional probabilities (i.e. supervised learning) for any of the random variables given the others. Bayesian inference is statistical inference in which evidence or observations are used to update or to newly infer the probability that a hypothesis may be true. ...
Unsupervised learning is also useful for data compression: fundamentally, all data compression algorithms either explicitly or implicitly rely on a probability distribution over a set of inputs. In computer science and information theory, data compression or source coding is the process of encoding information using fewer bits (or other information-bearing units) than an unencoded representation would use through use of specific encoding schemes. ... In mathematics, a probability distribution assigns to every interval of the real numbers a probability, so that the probability axioms are satisfied. ...
Another form of unsupervised learning is clustering, which is sometimes not probabilistic. Also see formal concept analysis. Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. ... The word probability derives from the Latin probare (to prove, or to test). ... Formal concept analysis is a method of data analysis that takes an input matrix specifying a set of objects and the properties thereof, and finds both all the natural clusters of properties and all the natural clusters of objects in the input data, where a natural property cluster is a...
Geoffrey Hinton is a British computer scientist most noted for his work on the mathematics and applications of neural networks, and their relationship to information theory. ... Terrence J. Sejnowski is an Investigator with the Howard Hughes Medical Institute and is the Francis Crick Professor at The Salk Institute for Biological Studies where he directs the Computational Neurobiology Laboratory. ... Simplified view of an artificial neural network A neural network is an interconnected group of biological neurons. ...
Categories: Machine learning An artificial neural network (ANN), also called a simulated neural network (SNN) or commonly just neural network (NN) is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. ... Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. ... In statistical computing, an expectation-maximization (EM) algorithm is an algorithm for finding maximum likelihood estimates of parameters in probabilistic models, where the model depends on unobserved latent variables. ... The self-organizing map (SOM) is a method for unsupervised learning, based on a grid of artificial neurons whose weights are adapted to match input vectors in a training set. ... In Statistics, GTM is basically a Bayesianised version of the Kohonnen network. ...
In unsupervisedlearning, all the observations are assumed to be caused by latent variables, that is, the observations are assumed to be at the end of the causal chain.
In supervised learning, one set of observations, called inputs, is assumed to be the cause of another set of observations, called outputs, while in unsupervisedlearning all observations are assumed to be caused by a set of latent variables.
The difficulty of the learning task increases exponentially in the number of steps between the two sets and that is why supervised learning cannot, in practice, learn models with deep hierarchies.
Machine learning overlaps heavily with statistics, since both fields study the analysis of data, but unlike statistics, machine learning is concerned with the algorithmic complexity of computational implementations.
Machine learning algorithms are organized into a taxonomy, based on the desired outcome of the algorithm.
One standard formulation of the supervised learning task is the classification problem: the learner is required to learn (to approximate the behavior of) a function which maps a vector
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