Machine Learning for the Quantified Self - On the Art of Learning from Sensory Data (Hardcover, 1st ed. 2018)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Cognitive Systems Monographs, 35
Release date:	October 2017
First published:	2018
Authors:	Mark Hoogendoorn • Burkhardt Funk
Dimensions:	235 x 155 x 20mm (L x W x T)
Format:	Hardcover
Pages:	231
Edition:	1st ed. 2018
ISBN-13:	978-3-319-66307-4
Categories:	Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning Promotions
LSN:	3-319-66307-0
Barcode:	9783319663074

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