introduction-basics-of-sensory-data.md (1397B)
1 +++ 2 title = 'Introduction & Basics of Sensory Data' 3 +++ 4 # Introduction & Basics of Sensory Data 5 In this course, use machine learning with self/sensory data. 6 7 "Quantified self": self-tracking of biological, physical, behavioral, environmental info. Driven by a goal of individual, they want to do something with the collected info. 8 9 Why? Health, better work performance...self-healing, self-discipline, self-design, self-association, self-entertainment. 10 11 Quantified self is different because sensory noise, missing measurements. It's temporal data and there's interaction with user. Use multiple datasets to learn. 12 13 Terminology: 14 - measurement: one value for one attribute at one time point 15 - time series: measurements in temporal order 16 - supervised learning: inferring function from set of labelled training data 17 - unsupervised learning: no target label, goal is to describe associations and patterns among attribute 18 - reinforcement learning: find optimal actions in given situation to maximize numerical reward later in time 19 20 ## Sensory data 21 Transforming raw data: combine tables by selecting step size Δt considered in data, start at earliest time point. Combine values of measurements within each interval [t, t+Δt) 22 23 Machine learning tasks: 24 - classification: predicting label (e.g. activity) based on sensors 25 - regression: predicting e.g. heart rate based on other sensory values and activity