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Arthur Lee Samuel (December 5, 1901 – July 29, 1990) [3] was an American pioneer in the field of computer gaming and artificial intelligence. [2] He popularized the term " machine learning " in 1959. [ 4 ]
Machine learning (ML) is a field of ... The term machine learning was coined in 1959 by Arthur Samuel, ... Multivariate linear regression extends the concept of ...
Linear regression is also a type of machine learning algorithm, more specifically a supervised algorithm, that learns from the labelled datasets and maps the data points to the most optimized linear functions that can be used for prediction on new datasets. [3]
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable (often called the outcome or response variable, or a label in machine learning parlance) and one or more error-free independent variables (often called regressors, predictors, covariates, explanatory ...
The simplest kind of artificial neural network is the linear network. It has been known for over two centuries as the method of least squares or linear regression. It was used as a means of finding a good rough linear fit to a set of points by Adrien-Marie Legendre (1805) [32] and Carl Friedrich Gauss (1795) [33] for the prediction of planetary ...
It’s widely understood that after machine learning models are deployed in production, the accuracy of the results can deteriorate over time. Arthur.ai launched in 2019 with the goal of helping ...
The basic form of a linear predictor function () for data point i (consisting of p explanatory variables), for i = 1, ..., n, is = + + +,where , for k = 1, ..., p, is the value of the k-th explanatory variable for data point i, and , …, are the coefficients (regression coefficients, weights, etc.) indicating the relative effect of a particular explanatory variable on the outcome.