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v. t. 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]
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.
Richard S. Sutton FRS FRSC is a Canadian computer scientist. He is a professor of computing science at the University of Alberta and a research scientist at Keen Technologies. [1] Sutton is considered one of the founders of modern computational reinforcement learning, [2] having several significant contributions to the field, including temporal ...
The word reactive hints at a ready response to events during the search through an internal online feedback loop for the self-tuning of critical parameters. Methodologies of interest for Reactive Search include machine learning and statistics, in particular reinforcement learning, active or query learning, neural networks, and metaheuristics.
t. e. The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen. The study of logic and formal reasoning from antiquity to the present led directly to the invention of the programmable digital computer in the 1940s, a machine ...
Development. 1990s. Major advances in all areas of AI, with significant demonstrations in machine learning, intelligent tutoring, case-based reasoning, multi-agent planning, scheduling, uncertain reasoning, data mining, natural language understanding and translation, vision, virtual reality, games, and other topics.
Deep learning is a subset of machine learning methods that utilize neural networks for representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data. The adjective "deep" refers to the use of multiple layers (ranging from three ...
www.cs.toronto.edu /~hinton /. Geoffrey Everest Hinton (born 6 December 1947) is a British-Canadian computer scientist, cognitive scientist, cognitive psychologist, known for his work on artificial neural networks which earned him the title as the "Godfather of AI". Hinton is University Professor Emeritus at the University of Toronto.