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  2. The Master Algorithm - Wikipedia

    en.wikipedia.org/wiki/The_Master_Algorithm

    The book outlines five approaches of machine learning: inductive reasoning, connectionism, evolutionary computation, Bayes' theorem and analogical modelling. The author explains these tribes to the reader by referring to more understandable processes of logic , connections made in the brain , natural selection , probability and similarity ...

  3. 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.

  4. Christopher Bishop - Wikipedia

    en.wikipedia.org/wiki/Christopher_Bishop

    Christopher Michael Bishop (born 7 April 1959) FRS FRSE FREng [7] is a British computer scientist. He is a Microsoft Technical Fellow and Director of Microsoft Research AI4Science. He is also Honorary Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College, Cambridge. Chris was a founding member of the UK AI ...

  5. Timeline of machine learning - Wikipedia

    en.wikipedia.org/wiki/Timeline_of_machine_learning

    1950s. Pioneering machine learning research is conducted using simple algorithms. 1960s. Bayesian methods are introduced for probabilistic inference in machine learning. [1] 1970s. ' AI winter ' caused by pessimism about machine learning effectiveness. 1980s.

  6. Outline of machine learning - Wikipedia

    en.wikipedia.org/wiki/Outline_of_machine_learning

    Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from an example training set of input observations in order to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.

  7. MLOps - Wikipedia

    en.wikipedia.org/wiki/MLOps

    MLOps. MLOps or ML Ops is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently. [1] The word is a compound of "machine learning" and the continuous delivery practice (CI/CD) of DevOps in the software field. Machine learning models are tested and developed in isolated experimental systems.

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