Inductive learning is a term from the fields of artificial intelligence, big data, and automation. It describes a method where computers learn from many examples or data to recognise general rules or patterns – without these rules being explicitly provided in detail beforehand.
In inductive learning, a computer is first fed with a lot of data. It „looks“ at this data and independently figures out what similarities or differences exist. From this, it creates its own rules. In contrast, deductive learning is where the rules are already known and set beforehand.
A clear example: Suppose a computer is given thousands of photos of apples and pears. Instead of being told exactly what identifies each apple or pear, it analyses the images and determines: apples are usually round and have a certain colour, pears generally look different. After enough examples, the system can eventually correctly classify new images independently.
Inductive learning is an important foundation for machines to operate autonomously and intelligently in industry, data analysis, or automation.
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