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Machine Learning: A Convergence of Emerging Technologies in Computing

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Book cover The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) (AMLTA 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 723))

Abstract

Machine Learning (ML) is the convergence of different disciplines in science and technology. While it is conceived, ML is part of computer science however, in its essence, it borrows or utilizes methods from other classic disciplines and mature computing theories and technologies, such as statistics, computational algorithms, optimization, and data mining. In this paper, we explore how these disciplines and technologies work hand in hand to prepare a passionate researcher gains a comprehensive perspective for being an ML expert. We have proposed a roadmap to show how different disciplines and technologies contribute to the ML foundation and we discuss each part of the roadmap separately. Moreover, to apply the proposed roadmap in practical terms, we also present how to use the proposed roadmap in the context of IoT and Fog Computing. The main contribution of this paper is to provide a guideline by developing a roadmap for foundational requirements of being a Machine Learning subject matter expert for the researchers or industry experts.

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References

  1. http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

  2. Brownlee, J.: Clever Algorithms: Nature-Inspired Programming Recipes. Lulu.com, Morrisville (2012)

    Google Scholar 

  3. Weise, T.: (2009). http://www.it-weise.de/projects/book.pdf

  4. Kelleher, J.D., Namee, B.M., D’Arcy, A.: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies. MIT Press, Cambridge (2015)

    MATH  Google Scholar 

  5. Brownlee, J.: (2013). http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/

  6. Steeves, J.: (2015). http://knowm.org/machine-learning-tools-an-overview/

  7. Pop, D., Iuhasz, G.: (2013). https://www.ieat.ro/wp-content/uploads/2013/05/technical_reports/IEAT-TR-2011-1.pdf

  8. Superwits Academy (2014). http://www.superwits.com/library/cloudsim-simulation-framework

  9. Open Fog Consortium. https://www.openfogconsortium.org/resources/#white-papers

  10. Bahl, V.: Microsoft Research (2015). https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/Micro-Data-Centers-mDCs-for-Mobile-Computing-1.pdf

  11. De, D.: Mobile Cloud Computing: Architectures, Algorithms and Applications. CRC Press, Boca Raton (2015)

    Google Scholar 

  12. Saharan, K.P., Kumar, A.: Fog in comparison to cloud: a survey. Int. J. Comput. Appl. (0975–8887) 122(3), 10–12 (2015)

    Google Scholar 

  13. https://en.wikipedia.org/wiki/Machine_learning

  14. Agarwal, S., Yadav, S., Yadav, A.K.: (2016). http://www.mecs-press.org/ijieeb/ijieeb-v8-n1/IJIEEB-V8-N1-6.pdf

  15. https://www.researchgate.net/file.PostFileLoader.html?id=57bea7b6217e20e33f730969&assetKey=AS%3A398883168505856%401472112566074

  16. Bittencourt, L.F., Diaz-Montes, J., Buyya, R., Rana, O.F., Parashar, M.: (2017). http://www.buyya.com/papers/MAS-Fog2017.pdf

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Author information

Authors and Affiliations

  1. Faculty of Continuing Education and Training, Seneca College, Toronto, Canada

    Morteza Kiadi

  2. School of Computing and Information Systems, Athabasca University, Athabasca, Canada

    Qing Tan

Authors
  1. Morteza Kiadi
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  2. Qing Tan
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Corresponding author

Correspondence to Qing Tan .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Computers and Information Sciences, Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

  3. Faculty of Computers and Information, Department of Computer Science and Engineering, Mansoura University, Dakahlia, Egypt

    Mohamed Elhoseny

  4. Arab Academy for Science, Technology and Maritime Transport (AASTMT), Dokki, Egypt

    Mohamed Mostafa

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Kiadi, M., Tan, Q. (2018). Machine Learning: A Convergence of Emerging Technologies in Computing. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_18

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  • DOI: https://doi.org/10.1007/978-3-319-74690-6_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74689-0

  • Online ISBN: 978-3-319-74690-6

  • eBook Packages: EngineeringEngineering (R0)

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