Journal international des progrès technologiques
Libre accès

Abstrait

Machine Learning Mathematical Models

Erica Melena

The primary scientific paradigm for modelling real-world systems and natural phenomena is the extraction of information from senses, formalisation of that information, and empirical verification of the model. Differential equations, for example, are used to express physical laws, chemical reactions, and dynamical behaviours, and verification can be thought of as predicting the procedure's future conditions. We conclude that adequate interactivities would benefit both professions. Information and methods collected for modelling physical phenomena in fields such as soft computing or material sciences are an initial source of knowledge for structuring effective learning systems, and the ML pattern could open up new horizons for modelling natural phenomena in the opposite direction. This is the core problem we address: how might general knowledge gleaned from a phenomenon modelling pattern assist in the development of efficient machine learning algorithms? In this case, both machine learning and mathematical models have distinct benefits, and in an ideal world, the two might be combined. The mathematical model's accurate nature and cheap computing