Machine Learning for the Quantified Self
On the Art of Learning from Sensory Data
Authors: Hoogendoorn, Mark, Funk, Burkhardt
Free Preview- 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
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- About this book
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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.
- Table of contents (10 chapters)
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Introduction
Pages 1-12
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Basics of Sensory Data
Pages 15-24
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Handling Noise and Missing Values in Sensory Data
Pages 25-50
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Feature Engineering Based on Sensory Data
Pages 51-70
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Clustering
Pages 73-100
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Table of contents (10 chapters)
- Download Preface 1 PDF (44.2 KB)
- Download Sample pages 2 PDF (485 KB)
- Download Table of contents PDF (215.1 KB)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Machine Learning for the Quantified Self
- Book Subtitle
- On the Art of Learning from Sensory Data
- Authors
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- Mark Hoogendoorn
- Burkhardt Funk
- Series Title
- Cognitive Systems Monographs
- Series Volume
- 35
- Copyright
- 2018
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer International Publishing AG
- eBook ISBN
- 978-3-319-66308-1
- DOI
- 10.1007/978-3-319-66308-1
- Hardcover ISBN
- 978-3-319-66307-4
- Softcover ISBN
- 978-3-319-88215-4
- Series ISSN
- 1867-4925
- Edition Number
- 1
- Number of Pages
- XV, 231
- Number of Illustrations
- 17 b/w illustrations, 72 illustrations in colour
- Topics
