Abstract
Following AI evolution in the previous chapter, this chapter is devoted to key concepts of machine learning (ML), deep learning (DL), and decision optimization (DO). We don’t go into the details on the 101 of these concepts or mathematical and statistical science behind these areas; instead, we are discussing considerations about their practical application in enterprises or other organizations. It should serve as a high-level introduction for readers with limited knowledge in this space.
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Notes
- 1.
See [1] for a short overview description of ML and [2] and [3] for a more comprehensive treatment of ML.
- 2.
See [4] for an introduction into reinforcement learning.
- 3.
See [5] for a short overview of the various RL algorithms.
- 4.
See [6] for a short introduction of classification algorithms.
- 5.
See [7] for a short introduction on clustering algorithms.
- 6.
See [8] for a short overview on dimensionality reduction.
- 7.
See [9] for a comprehensive and theoretical treatment of principal component analysis (PCA), including the mathematical background.
- 8.
See [10] for an in-depth treatment of DL and [11] for a more practical guide on DL.
- 9.
See [12] for a high-level overview of some of the most popular DLNs.
- 10.
See [13] for an in-depth treatment of CNNs.
- 11.
See [14] for a short overview on decision optimization.
- 12.
See [15] for a short overview on the integration of ML with DO.
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© 2020 Eberhard Hechler, Martin Oberhofer, Thomas Schaeck
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Hechler, E., Oberhofer, M., Schaeck, T. (2020). Key ML, DL, and DO Concepts. In: Deploying AI in the Enterprise. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-6206-1_3
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DOI: https://doi.org/10.1007/978-1-4842-6206-1_3
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-6205-4
Online ISBN: 978-1-4842-6206-1
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