Machine learning is a generic term for the artificial generation of knowledge from experience: An artificial system learns from examples and can generalize these after the end of the learning phase. To do this, machine learning algorithms build a statistical model based on training data. This means that the examples are not simply memorized, but patterns and regularities are recognized in the learning data. The system can also assess unknown data (learning transfer) or fail to learn unknown data (overfitting).
The topic is closely related to Knowledge Discovery in Databases and Data Mining, which, however, is primarily about finding new patterns and laws. Many algorithms can be used for both purposes. Methods of Knowledge Discovery in Databases can be used to produce or preprocess learning data for machine learning. In return, machine learning algorithms are used in data mining.