Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data

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Springer, 2017 M09 28 - 231 páginas
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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Contenido

1 Introduction
1
Part I Sensory Data and Features
13
2 Basics of Sensory Data
14
3 Handling Noise and Missing Values in Sensory Data
25
4 Feature Engineering Based on Sensory Data
51
Part II Learning Based on Sensory Data
71
5 Clustering
73
6 Mathematical Foundations for Supervised Learning
101
7 Predictive Modeling without Notion of Time
122
8 Predictive Modeling with Notion of Time
167
9 Reinforcement Learning to Provide Feedback and Support
203
Part III Discussion
215
10 Discussion
216
References
223
Index
229
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