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.

翻译：机器学习(ML)是指使机器能够从经验中学习概念的计算方法。在处理从数据实例、知识、限制、奖赏、对手和终身互动等各种广泛的经验时,当代ML/AI(人工智能)研究产生了许多学习范式和方法。尽管在所有不同的战线上不断取得进展,但不同而狭隘的集中方法也使得ML方法难以标准化、可配置和可再利用的开发,并排除了建立全方位学习各类经验的AI代理商的机会。这一条提出了标准化的ML形式主义,特别是学习目标的“标准方程式 ”,它提供了对许多受监督、不受监督、知识约束、强化、对抗和在线学习范式中许多重要的ML算法的统一理解。这些不同的算法被包含为不同选择模型组成部分的特殊案例。这个框架还为新ML方法的机械设计提供了指导,并成为通向具有各种经验的全景机器学习的有希望的工具。