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  2. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

    Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]

  3. Temporal difference learning - Wikipedia

    en.wikipedia.org/wiki/Temporal_difference_learning

    Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods , and perform updates based on current estimates, like dynamic programming methods.

  4. Independent component analysis - Wikipedia

    en.wikipedia.org/wiki/Independent_component_analysis

    In 1995, Tony Bell and Terry Sejnowski introduced a fast and efficient ICA algorithm based on infomax, a principle introduced by Ralph Linsker in 1987. A link exists between maximum-likelihood estimation and Infomax approaches. [24] A quite comprehensive tutorial on the maximum-likelihood approach to ICA has been published by J-F. Cardoso in ...

  5. Kernel method - Wikipedia

    en.wikipedia.org/wiki/Kernel_method

    Empirically, for machine learning heuristics, choices of a function that do not satisfy Mercer's condition may still perform reasonably if at least approximates the intuitive idea of similarity. [6] Regardless of whether k {\displaystyle k} is a Mercer kernel, k {\displaystyle k} may still be referred to as a "kernel".

  6. Automatic differentiation - Wikipedia

    en.wikipedia.org/wiki/Automatic_differentiation

    [3] [4] Presently, the two types are highly correlated and complementary and both have a wide variety of applications in, e.g., non-linear optimization, sensitivity analysis, robotics, machine learning, computer graphics, and computer vision. [5] [10] [3] [4] [11] [12] Automatic differentiation is particularly important in the field of machine ...

  7. Differentiable programming - Wikipedia

    en.wikipedia.org/wiki/Differentiable_programming

    Differentiable programming has found use in a wide variety of areas, particularly scientific computing and machine learning. [5] One of the early proposals to adopt such a framework in a systematic fashion to improve upon learning algorithms was made by the Advanced Concepts Team at the European Space Agency in early 2016. [6]

  8. Division algorithm - Wikipedia

    en.wikipedia.org/wiki/Division_algorithm

    Long division is the standard algorithm used for pen-and-paper division of multi-digit numbers expressed in decimal notation. It shifts gradually from the left to the right end of the dividend, subtracting the largest possible multiple of the divisor (at the digit level) at each stage; the multiples then become the digits of the quotient, and the final difference is then the remainder.

  9. Computational learning theory - Wikipedia

    en.wikipedia.org/wiki/Computational_learning_theory

    Online machine learning, from the work of Nick Littlestone [citation needed]. While its primary goal is to understand learning abstractly, computational learning theory has led to the development of practical algorithms. For example, PAC theory inspired boosting, VC theory led to support vector machines, and Bayesian inference led to belief ...