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

Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data (Cognitive Systems Monographs)9783319663074

Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data (Cognitive Systems Monographs)
by 作者: Mark Hoogendoorn - Burkhardt Funk
ISBN-10 书号: 3319663070
ISBN-13 书号: 9783319663074
Edition 版本: 1st ed. 2018
Publisher Finelybook 出版日期: 2017-09-29
Pages: 231


Book Description
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.
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


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