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Machine learning for the quantified ...

Funk, Burkhardt.

Machine learning for the quantified selfon the art of learning from sensory data /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for the quantified selfby Mark Hoogendoorn, Burkhardt Funk.
Reminder of title:	on the art of learning from sensory data /
Author:	Hoogendoorn, Mark.
other author:	Funk, Burkhardt.
Published:	Cham :Springer International Publishing :2018.
Description:	xv, 231 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	http://dx.doi.org/10.1007/978-3-319-66308-1
ISBN:	9783319663081$q(electronic bk.)

Machine learning for the quantified selfon the art of learning from sensory data /
Hoogendoorn, Mark.

Machine learning for the quantified selfon the art of learning from sensory data /[electronic resource] :by Mark Hoogendoorn, Burkhardt Funk. - Cham :Springer International Publishing :2018. - xv, 231 p. :ill., digital ;24 cm. - Cognitive systems monographs,v.351867-4925 ;. - Cognitive systems monographs ;v.16..

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.

ISBN: 9783319663081$q(electronic bk.)

Standard No.: 10.1007/978-3-319-66308-1doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .H66 2018

Dewey Class. No.: 006.31

Machine learning for the quantified selfon the art of learning from sensory data /
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520 $a 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.
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