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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. Recently, artificial neural networks have been able to surpass many previous approaches in performance.

  3. Neural network (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Neural_network_(machine...

    v. t. e. In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a model inspired by the structure and function of biological neural networks in animal brains. [1] [2] An ANN consists of connected units or nodes called artificial neurons, which loosely model the neurons in a brain.

  4. Rule-based machine learning - Wikipedia

    en.wikipedia.org/wiki/Rule-based_machine_learning

    v. t. e. Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. [1] [2] [3] The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that ...

  5. Feature learning - Wikipedia

    en.wikipedia.org/wiki/Feature_learning

    Feature learning is intended to result in faster training or better performance in task-specific settings than if the data was inputted directly, compare transfer learning. In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature ...

  6. Deep learning - Wikipedia

    en.wikipedia.org/wiki/Deep_learning

    Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised. [2]

  7. Reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Reinforcement_learning

    Reinforcement learning ( RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent ought to take actions in a dynamic environment in order to maximize the cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and ...

  8. Quantum machine learning - Wikipedia

    en.wikipedia.org/wiki/Quantum_machine_learning

    Quantum machine learning is the integration of quantum algorithms within machine learning programs.. The most common use of the term refers to machine learning algorithms for the analysis of classical data executed on a quantum computer, i.e. quantum-enhanced machine learning.

  9. Supervised learning - Wikipedia

    en.wikipedia.org/wiki/Supervised_learning

    Supervised learning. Supervised learning ( SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data on expected output values. [1]

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