Topic 2B Part 2 – Unsupervised Learning
There are three kinds of ML problems (or tasks) – this topic is split into three parts to explain each of them.
Supervised Learning is a method of training an algorithm where a data set consists of two parts: The data itself and a label. For example, a picture of a strawberry and a label which describes it as a strawberry.
The algorithm is rewarded when it predicts the correct label, and it can rearrange itself if it predicts the wrong label.
Similarly, the data can be associated with a real number, representing for example a geophysical parameter, rather than with a label. This is an example of regressions, where the task is to predict a continuous quantity.
This can be used to do two things:
- Classification: sorting data into classes
- Regression: parameter estimation
Featured Educators:
- Nicolo Taggio
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Featured Images and Example Data

Unsupervised machine learning methods
This is an example of unsupervised learning methods used for Imaging Mass Spectrometry (IMS)
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Nico Verbeeck, at al, 2019
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Sentinel-3 scans Earth's colour
Animation of Sentinel-3 using it's Ocean and Land Colour Instrument (OLCI) instrument
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ESA/ATG medialab
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