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Data Science Applications

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Communication Principles for Data Science

Part of the book series: Signals and Communication Technology ((SCT))

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Abstract

In Parts I & II, we have considered a communication problem wherein the goal is to deliver a sequence of information bits from a transmitter to a receiver over a channel. Two prominent channels were taken into consideration: (i) the additive white Gaussian channel (in Part I); and (ii) the wireline ISI channel (in Part II). What we did repeatedly throughout is: Given an encoding strategy (e.g., sequential coding, repetition coding), we strive to: Derive the optimal receiver for decoding the bit string; Analyze the decoding error probability when using the optimal receiver.

Communication principles are prevalent in data science.

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Notes

  1. 1.

    The upcoming sections of this book are updated from the author’s previous publication (Suh , 2022) but have been customized to suit the specific focus and logical flow of this book.

  2. 2.

    In machine learning, such a quantity is called the feature. This refers to a key component that well describes characteristics of data.

  3. 3.

    The word logistic comes from a Greek word which means a slow growth, like a logarithmic growth.

  4. 4.

    Sigmoid means resembling the lower-case Greek letter sigma, S-shaped.

  5. 5.

    We say that a symmetric matrix, say \(Q = Q^T \in {\mathbb {R}}^{d \times d}\), is positive semi-definite if \(v^T Q v \ge 0, \; \forall v \in {\mathbb {R}}^d\), i.e., all the eigenvalues of Q are non-negative. It is simply denoted by \(Q \succeq 0\).

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Authors and Affiliations

  1. KAIST, Daejeon, Korea (Republic of)

    Changho Suh

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  1. Changho Suh
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Correspondence to Changho Suh .

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Suh, C. (2023). Data Science Applications. In: Communication Principles for Data Science. Signals and Communication Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-8008-4_3

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  • DOI: https://doi.org/10.1007/978-981-19-8008-4_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8007-7

  • Online ISBN: 978-981-19-8008-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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