Machine Learning for the Quantified Self

Mark Hoogendoorn|Burkhardt Funk

ISBN 10: 3319663070 / ISBN 13: 9783319663074

Published by Springer International Publishing, 2017

About this Item

Presents a unique overview of dedicated machine learning techniques for sensor dataFeatures hands-on exercises, including those related to mobile app developmentIllustrates the techniques by means of examples to make them more easily unders.

Seller Inventory # 155914928

Bibliographic Details

• Title: Machine Learning for the Quantified Self
• Publisher: Springer International Publishing
• Publication Date: 2017
• Language: English
• ISBN 10: 3319663070
• ISBN 13: 9783319663074
• Binding: Hardcover
• Condition: New
• Seller catalogs:
  • Mathematik/Naturwissenschaften/Technik/Medizin

About this title

Synopsis

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.

From the Back Cover

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.