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Machine learning and deep learning

Author

Listed:
  • Christian Janiesch

    (University of Würzburg)

  • Patrick Zschech

    (Friedrich-Alexander University Erlangen-Nürnberg)

  • Kai Heinrich

    (Otto-von-Guericke-Universität Magdeburg)

Abstract

Today, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.

Suggested Citation

  • Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    • Handle: RePEc:spr:elmark:v:31:y:2021:i:3:d:10.1007_s12525-021-00475-2
    DOI: 10.1007/s12525-021-00475-2
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    References listed on IDEAS

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    1. Shrestha, Yash Raj & Krishna, Vaibhav & von Krogh, Georg, 2021. "Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges," Journal of Business Research, Elsevier, vol. 123(C), pages 588-603.
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    3. Chen, Serena H. & Jakeman, Anthony J. & Norton, John P., 2008. "Artificial Intelligence techniques: An introduction to their use for modelling environmental systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 379-400.
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    5. Marcus Fischer & David Heim & Adrian Hofmann & Christian Janiesch & Christoph Klima & Axel Winkelmann, 2020. "A taxonomy and archetypes of smart services for smart living," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(1), pages 131-149, March.
    6. Thomas Spooner & John Fearnley & Rahul Savani & Andreas Koukorinis, 2018. "Market Making via Reinforcement Learning," Papers 1804.04216, arXiv.org.
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    More about this item

    Keywords

    Machine learning; Deep learning; Artificial intelligence; Artificial neural networks; Analytical model building;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights

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