Machine Learning for the Quantified Self : On the Art of Learning from Sensory Data
1st ed. 2018
Book Details
Format
Hardback or Cased Book
Book Series
Cognitive Systems Monographs
ISBN-10
3319663070
ISBN-13
9783319663074
Edition
1st ed. 2018
Publisher
Springer International Publishing AG
Imprint
Springer International Publishing AG
Country of Manufacture
CH
Country of Publication
GB
Publication Date
Oct 5th, 2017
Print length
231 Pages
Weight
526 grams
Dimensions
16.50 x 24.50 x 2.00 cms
Product Classification:
Machine learning
Ksh 25,200.00
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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.
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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