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Introduction to Machine Learning

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Machine Learning for Practical Decision Making

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 334))

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Abstract

The last two decades have seen a quiet but important revolution in computer science. Now more than ever, computers and algorithms are leading to more prosperous and more accurate insights with software that learns from experience and adapts automatically to match the needs of its tasks [1]. Formerly, the programmer decided how the system would work by manually writing the code. Today, we do not write programs but rather collect data consisting of instruction insights, and develop the algorithms changes that manipulate it as necessary to extract patterns and insights. Today, we have programs that can recognize faces and fingerprints, understand speech, translate, navigate, drive a car, recommend movies, and many more [1]. This is possible now because of artificial intelligence (AI) and its fields, mainly machine learning.

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Authors and Affiliations

  1. School of Health Policy and Management, York University, Toronto, ON, Canada

    Christo El Morr

  2. School of Information Technology, York University, Toronto, ON, Canada

    Manar Jammal

  3. Department of International Studies, York University, Glendon Campus, Toronto, ON, Canada

    Hossam Ali-Hassan

  4. Ontario Health, Toronto, ON, Canada

    Walid El-Hallak

Authors
  1. Christo El Morr
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  2. Manar Jammal
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  4. Walid El-Hallak
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El Morr, C., Jammal, M., Ali-Hassan, H., El-Hallak, W. (2022). Introduction to Machine Learning. In: Machine Learning for Practical Decision Making. International Series in Operations Research & Management Science, vol 334. Springer, Cham. https://doi.org/10.1007/978-3-031-16990-8_1

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