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Machine Learning for the Quantified Self - Summary Slides
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A summary of all the slides for the course Machine Learning for the Quantified Self, MSc AI.
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Machine Learning for the Quantified Self - Slides SummaryLecture 1: Introduction and Basics of Sensory Data
Quantified Self definition
● Term first coined by Gary Wolf and Kevin Kelly in Wired Magazine
● Swan (2013): “The quantified self is any individual engaged in the self-tracking of any
kind of biological, physical, behavioral, or environmental information. There is a proactive
stance toward obtaining information and acting on it.”
● We: “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.”
Quantified Self: measurements
● Augemberg (2012):
Quantified Self: why?
● Choe, 2014:
○ Interview with 52 quantified selves
○ Three 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:
○ Identify “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)
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,Machine Learning for the Quantified Self - Slides Summary
Quantified Self: Arnold and Bruce
● Use two running examples
○ Arnold:
■ Loves sports
■ Wants to participate in IRONMAN
■ Gadget freak
■ Smart phone/watch/...
■ Electronic scale
■ Chest strap
■ …...
○ Bruce:
■ Diabetic
■ Susceptible for depression
■ Smart watch
■ Device to measure blood glucose level
■ ......
Moving on the machine learning
● Machine learning: “Machine learning is to automatically identify patterns from data”
● What could we learn for Arnold and Bruce?
○ Arnold:
■ Advising the training to make most progress towards a certain goal based
on past outcomes of training.
■ Forecasting when a certain running distance will be feasible based on the
progress made so far and the training schedule.
○ Bruce:
■ Predict the next blood glucose level based on past measurements and
activity levels.
■ Determine when and how to intervene when the mood is going down to
avoid a spell of depression.
■ Finding clusters of locations that appear to elevate one’s mood.
Why is the Quantified Self so different?
● Sensory noise
● Missing measurements
● Temporal data
● Interaction with a user
● Learn over multiple datasets
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,Machine Learning for the Quantified Self - Slides Summary
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.
● Machine learning terminology is assumed to be known, for your convenience:
○ Supervised learning is the machine learning task of inferring a function from a
set of labeled training data
○ In unsupervised learning, there is no target measure (or label), and the goal is
to describe the associations and patterns among the attributes
○ 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
Mathematical notation
3
, Machine Learning for the Quantified Self - Slides Summary
Overview of the course
Dataset
● During the course we will use a running example provided by CrowdSignals.io
● People share their mobile sensor data (smart phone and smart watch) and get paid for
annotating their data with activities
Mobile phone measurements (examples)
● Accelerometer
○ Measures the changes in forces upon the phone in the x-y-z plane
● Gyroscope
○ Orientation of the phone compared to the earth’s surface
● Magnetometer
○ Measures x-y-z orientation compared to the earth’s magnetic field
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