The practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers

The process of using statistics and techniques related to statistics in order to understand and learn from your data so that you can predict its future.

In data communications, flow control is the process of managing the rate of data transmission between two nodes to prevent a fast sender from overwhelming a slow receiver. It provides a mechanism for the receiver to control the transmission speed, so that the receiving node is not overwhelmed with data from transmitting node.

Methods and techniques used either in machine learning or statistical learning

A non-parametric supervised learning method used for classification and regression. The goal is to create a tree-like graph or model of decisions and their possible consequences by learning simple decision rules inferred from the data features.

A computational model based on a large collection of simple neural units (artificial neurons), loosely analogous to the observed behavior of a biological brain's axons. These systems are self-learning and trained, rather than explicitly programmed.

Any method that uses multiple learning algorithms in an attempt to improve predictive performance not obtainable otherwise with any of the constituent learning algorithms.

A statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors').

A branch of machine learning based on deep neural networks. A deep neural network (DNN) is an artificial neural network (ANN) with multiple hidden layers of units between the input and output layers.

A machine learning method used in recognising relationships among variables in databases and extracted in the form of rules.

Any method used in clustering or cluster analysis, i.e. in grouping a set of objects in such a way that objects in the same group (cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters).

A process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting.

A method based on reducing the number of random variables under consideration, via obtaining a set of principal variables.

A family of learning algorithms that, instead of performing explicit generalization, compares new problem instances with instances seen in training, which have been stored in memory.

A method in probability and statistics based on Bayes' theorem, mainly related to statistical inference.

Any method used in pattern analysis that relies on kernel functions, which enable it to operate in a high-dimensional, implicit feature space without ever computing the coordinates of the data in that space, but rather by simply computing the inner products between the images of all pairs of data in the feature space.

A method of data analysis that automates model building. Using algorithms that iteratively learn from data, machine learning allows computers to find hidden insights without being explicitly programmed where to look.

A type of method that makes use of set(s) of rules to perform the relevant task.