Inductive learning is a term used in the fields of artificial intelligence, big data and automation. It describes a method in which computers learn from many examples or data in order to recognise general rules or patterns - without these rules having been specified to them in detail beforehand.
With inductive learning, a computer is first fed a lot of data. It "looks" at this data and independently finds out what similarities or differences there are. The machine uses this to create its own rules. This contrasts with "deductive learning", in which the rules are already known and defined in advance.
An illustrative example: Suppose a computer receives thousands of photos of apples and pears. Instead of telling it exactly how to recognise each apple or pear, it analyses the images and determines that apples are usually round and have a certain colour, while pears usually look different. After enough examples, the system will eventually be able to recognise new pictures correctly on its own.
Inductive learning is an important basis for enabling machines in industry, data analysis and automation to work independently and intelligently.
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