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Machine Learning for the Quantified Self

On the Art of Learning from Sensory Data

  • Book
  • © 2018

Overview

Authors:
  1. Mark Hoogendoorn

    1. Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

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    You can also search for this author in PubMed   Google Scholar

  2. Burkhardt Funk

    1. Institut für Wirtschaftsinformatik, Leuphana Universität Lüneburg, Lüneburg, Germany

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Presents a unique overview of dedicated machine learning techniques for sensor data
  • Features hands-on exercises, including those related to mobile app development
  • Illustrates the techniques by means of examples to make them more easily understandable
  • Includes supplementary material: sn.pub/extras

Part of the book series: Cognitive Systems Monographs (COSMOS, volume 35)

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Table of contents (10 chapters)

  1. Front Matter

    Pages i-xv
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  2. Introduction

    • Mark Hoogendoorn, Burkhardt Funk
    Pages 1-12

  3. Sensory Data and Features

    1. Front Matter

      Pages 13-13
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    2. Basics of Sensory Data

      • Mark Hoogendoorn, Burkhardt Funk
      Pages 15-24

    3. Handling Noise and Missing Values in Sensory Data

      • Mark Hoogendoorn, Burkhardt Funk
      Pages 25-50

    4. Feature Engineering Based on Sensory Data

      • Mark Hoogendoorn, Burkhardt Funk
      Pages 51-70

  4. Learning Based on Sensory Data

    1. Front Matter

      Pages 71-71
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    2. Clustering

      • Mark Hoogendoorn, Burkhardt Funk
      Pages 73-100

    3. Mathematical Foundations for Supervised Learning

      • Mark Hoogendoorn, Burkhardt Funk
      Pages 101-121

    4. Predictive Modeling without Notion of Time

      • Mark Hoogendoorn, Burkhardt Funk
      Pages 123-165

    5. Predictive Modeling with Notion of Time

      • Mark Hoogendoorn, Burkhardt Funk
      Pages 167-202

    6. Reinforcement Learning to Provide Feedback and Support

      • Mark Hoogendoorn, Burkhardt Funk
      Pages 203-214

  5. Discussion

    1. Front Matter

      Pages 215-215
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    2. Discussion

      • Mark Hoogendoorn, Burkhardt Funk
      Pages 217-221

  6. Back Matter

    Pages 223-231
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Keywords

About this book

This book explains the complete loop to effectively use self-tracking data for machine learning. While it focuses on self-tracking data, the techniques explained are also applicable to sensory data in general, making it useful for a wider audience. Discussing concepts drawn from from state-of-the-art scientific literature, it illustrates the approaches using a case study of a rich self-tracking data set. Self-tracking has become part of the modern lifestyle, and the amount of data generated by these devices is so overwhelming that it is difficult to obtain useful insights from it. Luckily, in the domain of artificial intelligence there are techniques that can help out: machine-learning approaches allow this type of data to be analyzed. While there are ample books that explain machine-learning techniques, self-tracking data comes with its own difficulties that require dedicated techniques such as learning over time and across users.

Authors and Affiliations

  • Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Mark Hoogendoorn

  • Institut für Wirtschaftsinformatik, Leuphana Universität Lüneburg, Lüneburg, Germany

    Burkhardt Funk

Bibliographic Information

  • Book Title: Machine Learning for the Quantified Self

  • Book Subtitle: On the Art of Learning from Sensory Data

  • Authors: Mark Hoogendoorn, Burkhardt Funk

  • Series Title: Cognitive Systems Monographs

  • DOI: https://doi.org/10.1007/978-3-319-66308-1

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing AG 2018

  • Hardcover ISBN: 978-3-319-66307-4Published: 05 October 2017

  • Softcover ISBN: 978-3-319-88215-4Published: 15 August 2018

  • eBook ISBN: 978-3-319-66308-1Published: 28 September 2017

  • Series ISSN: 1867-4925

  • Series E-ISSN: 1867-4933

  • Edition Number: 1

  • Number of Pages: XV, 231

  • Number of Illustrations: 17 b/w illustrations, 72 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence

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