Machine Learning for the Quantified Self (XM_40012)
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Summary - Machine Learning for the Quantified Self (XM_40012)
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Course
Machine Learning for the Quantified Self (XM_40012)
Institution
Vrije Universiteit Amsterdam (VU)
Summary of all lectures in the course Machine Learning for the Quantified Self, taught at the Vrije Universiteit (VU) at the Master's in Computer Science.
Machine Learning for the Quantified Self (XM_40012)
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Chapter 1. Introduction
The term quantified self was first coined by Gary Wolf and Kevin Kelley in Wired Magazine.
“The quantified self is any individual engaged in the self-tracking of any kind of biological,
physical, behavioral, or environmental information. The self-tracking is driven by a certain
goal of the individual with a desire to act upon the collected information.”
A number of measurements fall under this quantified self (biological, physical, behavioral,
environmental, etc). According to Augemberg (2012), they fall into one of the following
categories:
Choe (2014) researched why people are tracking themselves. This research consisted of 52
interviews, finding out three main categories:
- Improved health (cure or manage a condition, execute a treatment plan, achieve a
goal)
- Improve other aspects of life (maximize work performance, be mindful)
- Find new life experiences (have fun, learn new things)
Gimpel (2013) identified a “Five-Factor Framework of Self-Tracking Motivations”:
- Self-healing (become healthy)
- Self-discipline (rewarding aspects of it)
- Self-design (control and optimize “yourself”)
- Self-association (associated with movement)
- Self-entertainment (entertainment value)
Throughout the course, there will be two examples of quantified selves, Arnold, and Bruce:
,Basic terminology:
- A measurement is one value for an attribute recorded at a specific time point.
- A time series is a series of measurements in temporal order.
- We distinguish different types of machine learning:
o Supervised learning is the machine learning task of inferring a function from a
set of labeled training data.
o In unsupervised learning, there is no target measure (or label), and the goal is
to describe the associations and patterns among the attributes.
o Reinforcement learning tries to find optimal actions in a given situation so as
to maximize a numerical reward that does not immediately come with the
action but later in time.
, Chapter 2. Sensory Data
In a mobile phone, there are many sensors, for example:
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