Machine learning

scientific study of algorithms and statistical models that computer systems use to perform tasks without explicit instructions

Machine learning (ML) is the science and technology of developing and applying computer algorithms that improve automatically through comparing algorithmic results with training data using logically precise rules for success and by the use of extensive data sets. ML is one the major subdomains of artificial intelligence.

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  • Thanks to the recent advances in processing speed, data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story — ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen.
    • D. Gündüz, P. de Kerret, N. D. Sidiropoulos, D. Gesbert, C. R. Murthy and M. van der Schaar: "Machine Learning in the Air," in IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2184-2199, Oct. 2019, doi:10.1109/JSAC.2019.2933969
  • The future trends are illustrated by two case studies. The first describes a recently developed method for dealing with reliability of decisions of classifiers, which seems to be promising for intelligent data analysis in medicine. The second describes an approach to using machine learning in order to verify some unexplained phenomena from complementary medicine, which is not (yet) approved by the orthodox medical community but could in the future play an important role in overall medical diagnosis and treatment.
    • Igor Kononenko: (2001). "Machine learning for medical diagnosis: History, state of the art and perspective". Artificial Intelligence in Medicine 23 (1): 89–109. DOI:10.1016/S0933-3657(01)00077-X.
  • Machine learning can be broadly defined as computational methods using experience to improve performance or to make accurate predictions. Here, experience refers to the past information available to the learner, which typically takes the form of electronic data collected and made avalaible for analysis. This data could be in the form of digitized human-labeled training sets, or other types of information obtained via interactin with the environment. In all cases, its quality and size are crucial to the success of the predictions made by the learner.
  • Years after Simons's team at Renaissance adopted machine-learning techniques, other quants have begun to embrace these methods. Renaissance anticipated a transformation in decision-making that's sweeping almost every business and walk of life. More companies and individuals are accepting and embracing models that continuously learn from their successes and failures. As investor Matthew Granade has noted, Amazon, Tencent, Netflix, and others that rely on dynamic, ever-changing models are emerging dominant. The more data that's fed to the machines, the smarter they're supposed to become.

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