Machine Learning for the Quantified Self

On the Art of Learning from Sensory Data

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing
Cover of the book Machine Learning for the Quantified Self by Mark Hoogendoorn, Burkhardt Funk, Springer International Publishing
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Author: Mark Hoogendoorn, Burkhardt Funk ISBN: 9783319663081
Publisher: Springer International Publishing Publication: September 28, 2017
Imprint: Springer Language: English
Author: Mark Hoogendoorn, Burkhardt Funk
ISBN: 9783319663081
Publisher: Springer International Publishing
Publication: September 28, 2017
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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