Machine Learning for the Quantified Self

On the Art of Learning from Sensory Data

Mark Hoogendoorn, Burkhardt Funk (Autoren)

Buch | Hardcover

XV, 231 Seiten

2017 | 1st ed. 2018
Springer International Publishing (Verlag)
9783319663074 (ISBN)

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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. 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.

Introduction.- Basics of Sensory Data.- Feature Engineering based on Sensory Data.- Predictive Modeling without Notion of Time.- Predictive Modeling with Notion of Time.- Reinforcement Learning to Provide Feedback and Support.- Discussion.

Erscheinungsdatum	13.10.2017
Reihe/Serie	Cognitive Systems Monographs
Zusatzinfo	XV, 231 p. 89 illus., 72 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Gewicht	496 g
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Themenwelt	Technik
Schlagworte	Artificial Intelligence • artificial intelligence (incl. robotics) • Cognitive Systems • Computational Intelligence • Engineering • Engineering: general • Learning from Sensory Data • machine learning • Personalized m-health • Quantified Self • Robotics
ISBN-13	9783319663074 / 9783319663074
Zustand	Neuware