Machine Learning for the Quantified Self On the Art of Learning from Sensory Data / by Mark Hoogendoorn, Burkhardt Funk.
By: Hoogendoorn, Mark, autor
Contributor(s): Funk, Burkhardt, autor
Material type:
E-bookSeries: (Cognitive Systems Monographs, 1867-4925; 35); (Engineering (Springer-11647)).Publisher: Cham : Springer International Publishing, 2018Description: 1 recurso en línea (XV, 231 páginas 89 ilustraciones, 72 ilustraciones a color).ISBN: 9783319663081.Subject: | Robótica
Online resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)
Abstract: 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.
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|
LIBRO-E NO PRÉSTAMO
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Madrid Digital Acceso Electrónico (UEM) | Ciencias e Ingeniería | Q325.5 H664 2018 EB (Browse shelf(Opens below)) | Acceso electrónico | eBook.15112693 |
Browsing Madrid Digital shelves, Shelving location: Acceso Electrónico (UEM) Close shelf browser (Hides shelf browser)
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| Q325.5 D475 2015 EB Grammar-Based Feature Generation for Time-Series Prediction | Q325.5 ES Machine Learning | Q325.5 H477 2016 EB Multiple Instance Learning : Foundations and Algorithms | Q325.5 H664 2018 EB Machine Learning for the Quantified Self On the Art of Learning from Sensory Data | Q325.5 H863 2016 EB Human Activity Recognition and Prediction | Q325.5 .I65 2016 EB Knowledge Transfer between Computer Vision and Text Mining : Similarity-based Learning Approaches | Q325.5 J393 2016 EB Twin support vector machines : models, extensions and applications |
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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