Machine Learning for the Quantified Self
On the Art of Learning from Sensory Data
Samenvatting
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.
Trefwoorden
machine learning sensory data data-analyse quantified self predictive modeling clustering feature engineering time series analysis neural networks data preprocessing reinforcement learning supervised learning missing values outlier detection wearable sensors unsupervised learning data streams kalman filter fourier transform principal component analysis k-nearest neighbors support vector machines decision trees naive bayes recurrent neural networks ensemble methods arima
Trefwoorden
Specificaties
Inhoudsopgave
U kunt van deze inhoudsopgave een PDF downloaden
Basics of Sensory Data
Handling Noise and Missing Values in Sensory Data
Feature Engineering Based on Sensory Data
Clustering
Mathematical Foundations for Supervised Learning
Predictive Modeling without Notion of Time
Predictive Modeling with Notion of Time
Reinforcement Learning to Provide Feedback and Support
Discussion
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