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Machine learning in the real world

Published: 01 September 2016 Publication History

Abstract

Machine Learning (ML) has become a mature technology that is being applied to a wide range of business problems such as web search, online advertising, product recommendations, object recognition, and so on. As a result, it has become imperative for researchers and practitioners to have a fundamental understanding of ML concepts and practical knowledge of end-to-end modeling. This tutorial takes a hands-on approach to introducing the audience to machine learning. The first part of the tutorial gives a broad overview and discusses some of the key concepts within machine learning. The second part of the tutorial takes the audience through the end-to-end modeling pipeline for a real-world income prediction problem.

References

[1]
ML Pipelines. https://amplab.cs.berkeley.edu/ml-pipelines/, 2014. {Online; accessed 17-July-2016}.
[2]
D. Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press, New York, NY, USA, 2012.
[3]
C. M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006.
[4]
T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer Series in Statistics. Springer New York Inc., New York, NY, USA, 2001.
[5]
T. M. Mitchell. Machine Learning. McGraw-Hill, Inc., New York, NY, USA, 1 edition, 1997.
[6]
K. P. Murphy. Machine Learning: A Probabilistic Perspective. The MIT Press, 2012.

Cited By

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  • (2023)MLflow2PROV: Extracting Provenance from Machine Learning ExperimentsProceedings of the Seventh Workshop on Data Management for End-to-End Machine Learning10.1145/3595360.3595859(1-4)Online publication date: 18-Jun-2023
  • (2023)Extracting Provenance of�Machine Learning Experiment Pipeline ArtifactsAdvances in Databases and Information Systems10.1007/978-3-031-42914-9_17(238-251)Online publication date: 4-Sep-2023

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    Published In

    cover image Proceedings of the VLDB Endowment
    Proceedings of the VLDB Endowment  Volume 9, Issue 13
    September 2016
    378 pages
    ISSN:2150-8097
    Issue’s Table of Contents

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    VLDB Endowment

    Publication History

    Published: 01 September 2016
    Published in PVLDB Volume 9, Issue 13

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    • (2023)MLflow2PROV: Extracting Provenance from Machine Learning ExperimentsProceedings of the Seventh Workshop on Data Management for End-to-End Machine Learning10.1145/3595360.3595859(1-4)Online publication date: 18-Jun-2023
    • (2023)Extracting Provenance of�Machine Learning Experiment Pipeline ArtifactsAdvances in Databases and Information Systems10.1007/978-3-031-42914-9_17(238-251)Online publication date: 4-Sep-2023

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