Machine learning for the quantified self : on the art of learning from sensory data / Mark Hoogendoorn, Burkhardt Funk.

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Machine learning for the quantified self : on the art of learning from sensory data / Mark Hoogendoorn, Burkhardt Funk.

9783319663081 (electronic book)
3319663089 (electronic book)
9783319663074
3319663070

Cham, Switzerland : Springer, [2018]

©2018

English

1 online resource.

10.1007/978-3-319-66308-1 doi

Q325.5

006.3/1
620

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 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 sample 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.

Includes bibliographical references and index.

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text file PDF

Online resource; title from PDF title page (SpringerLink, viewed October 9, 2017).

Cognitive systems monographs ; v. 35.

Print version: 9783319663074

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