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  2. Timeline of machine learning - Wikipedia

    en.wikipedia.org/wiki/Timeline_of_machine_learning

    Summary pre-1950: Statistical methods are discovered and refined. 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

  3. Our America - Wikipedia

    en.wikipedia.org/wiki/Our_America

    Our America is a film based on the book Our America: Life And Death on the South Side of Chicago. [1] It premiered at the 2002 Sundance Film Festival , and aired on American television later that year.

  4. Machine learning - Wikipedia

    en.wikipedia.org/wiki/Machine_learning

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

  5. The Alignment Problem - Wikipedia

    en.wikipedia.org/wiki/The_Alignment_Problem

    The Alignment Problem: Machine Learning and Human Values is a 2020 non-fiction book by the American writer Brian Christian.It is based on numerous interviews with experts trying to build artificial intelligence systems, particularly machine learning systems, that are aligned with human values.

  6. Neural network (machine learning) - Wikipedia

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

    Furthermore, researchers involved in exploring learning algorithms for neural networks are gradually uncovering generic principles that allow a learning machine to be successful. For example, Bengio and LeCun (2007) wrote an article regarding local vs non-local learning, as well as shallow vs deep architecture. [214]

  7. 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 judgments.

  8. Outline of machine learning - Wikipedia

    en.wikipedia.org/wiki/Outline_of_machine_learning

    Machine learning – a subfield of soft computing within computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. [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 ]

  9. Active learning (machine learning) - Wikipedia

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

    e. Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...

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