Any change in a system that al lows it to perform better the second time o n repet ition of the same t ask or on anot her task drawn from the same population.

Depending on the amount and type of knowledge available to the system before the learning phase (system’s a priori knowledge) we can distinguish several situations:

• The simplest form of learning is just assigning values to specified parameters. This is a situation when the system contains all the knowledge required for a particular type of tasks.
• Another rudimentary type of learning is storing data as it is. This is called rote learning. An example of this type of learning is filling a database.
• The process of knowledge acquisition in an expert system is a kind of learning task where some pre-defined structures (rules, frames etc.) are filled with data specified directly or indirectly by an expert. In this case only the structure of the knowledge is known.
• The system is given a set of examples (training data) and it is supposed to create a description of this set in terms of a particular language. The a priori knowledge of the system is the syntax of the allowed language (syntactical bias) and possibly some characteristics of the domain from which the examples are drawn (domain knowledge or semantic bias). This is a typical task for Inductive learning and is usually called Concept learning or Learning from examples.
• There are learning systems (e.g.Neural networks) which given no a priori knowledge can learn to react properly to the data. Neural networks actually use a kind of a pre-defined structure of the knowledge to be represented (a network of neuron-like elements), which however is very general and thus suitable for various kinds of knowledge.

As in human learning the process of machine learning is affected by the presence (or absence) of a teacher.