Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data
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1 ed. Cham: Springer International Publishing, 2018. 231 p. (Cognitive Systems Monograph; Vol. 35).
Research output: Books and anthologies › Monographs › Research › peer-review
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RIS
TY - BOOK
T1 - Machine Learning for the Quantified Self
T2 - On the Art of Learning from Sensory Data
AU - Hoogendoorn, Mark
AU - Funk, Burkhardt
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Business informatics
KW - Cognitive systems
KW - Machine Learning
KW - Quantified Self
KW - Learning from Sensory Data
KW - Personalized m-health
U2 - 10.1007/978-3-319-66308-1
DO - 10.1007/978-3-319-66308-1
M3 - Monographs
SN - 978-3-319-66307-4
SN - 3-319-66307-0
T3 - Cognitive Systems Monograph
BT - Machine Learning for the Quantified Self
PB - Springer International Publishing
CY - Cham
ER -