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

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.

Table of Contents

Chapter 1 Introduction
Part I Sensory Data and Features
Chapter 2 Basics Of Sensory Data
Chapter 3 Handling Noise And Missing Values In Sensory Data
Chapter 4 Feature Engineering Based On Sensory Data

Part II Learning Based on Sensory Data
Chapter 5 Clustering
Chapter 6 Mathematical Foundations For Supervised Learning
Chapter 7 Predictive Modeling Without Notion Of Time
Chapter 8 Predictive Modeling With Notion Of Time
Chapter 9 Reinforcement Learning To Provide Feedback And Support

Part III Discussion
Chapter 10 Discussion