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

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