Machine learning (ML) is a collection of mathematical methods for pattern recognition. These methods recognize patterns, for example, by the best possible decomposition of data sets into hierarchical structures that are aligned to the best possible entropy. Or, similarities between datasets are determined via vectors and patterns are inferred from them, trained or untrained. Machine learning algorithms are indeed capable of solving many everyday or even very specific problems. However, in a machine learning developer's practice, problems often arise when there is either too little data or the data has too many dimensions. Entropy-driven learning algorithms such as decision trees become too complex when there are many dimensions and vectors.
