Seong-lae ChoFunction approximation: learning algorithm using data
The term "machine learning" was first coined by Arthur Samuel in 1959.
The aim of Machine Learning is building a Statistical Model that works well on novel data through Model Generalization.
Future prediction is integration, and rule decomposition is differentiation. Derivation is the process of obtaining local rules (direction/velocity/slope), while Integral is the process of advancing the state forward according to those rules. In other words, because differentiation is definable, it provides guidance on how to handle each specific data sample or each instance of reality. Inference is integration; it approximates the uncertain future prediction. Therefore, training is differentiation and inference is integration.
Machine Learning Types
Machine Learning Engineering