  Machine learning (ML) is about computational methods that enable machines to
learn concepts from experience. In handling a wide variety of experience
ranging from data instances, knowledge, constraints, to rewards, adversaries,
and lifelong interaction in an ever-growing spectrum of tasks, contemporary
ML/AI (artificial intelligence) research has resulted in a multitude of
learning paradigms and methodologies. Despite the continual progresses on all
different fronts, the disparate narrowly focused methods also make
standardized, composable, and reusable development of ML approaches difficult,
and preclude the opportunity to build AI agents that panoramically learn from
all types of experience. This article presents a standardized ML formalism, in
particular a `standard equation' of the learning objective, that offers a
unifying understanding of many important ML algorithms in the supervised,
unsupervised, knowledge-constrained, reinforcement, adversarial, and online
learning paradigms, respectively -- those diverse algorithms are encompassed as
special cases due to different choices of modeling components. The framework
also provides guidance for mechanical design of new ML approaches and serves as
a promising vehicle toward panoramic machine learning with all experience.
