While humanity has established a comprehensive descriptive system for machine learning, our understanding of human learning remains largely at a vague, intuitive level. Even basic questions like "What does learning do?", "What is knowledge?", and "How do we know when we've learned something?" are often answered intuitively, lacking precise description—much like how, before the advent of physics, people explained natural phenomena solely through intuition. Without a precise descriptive system, it's difficult to effectively analyze and improve learning. This book aims to establish a systematic framework for learning, mimicking how physics describes natural phenomena and medicine describes the human body and diseases, systematically classifying and defining learning objectives, knowledge types, material types, learning methods, verification methods, and failure types.
如今人类已经为机器学习建立了完整的描述体系，但对人类自身的学习却仍停留在模糊的直觉层面，连“学习是在做什么”“知识是什么”“怎样才算学会”等基本问题，往往只能凭感觉回答，无法准确描述——就如同在物理学出现之前，人们仅凭直觉解释自然现象一样。缺乏精确的描述体系，就难以有效分析和改进学习。本书旨在为学习建立一套系统化框架，仿照物理学描述自然现象、医学描述人体与疾病的方式，对学习的目标、知识类型、材料类型、学习方式、验证手段及失败类型，进行系统的分类与定义。
如今人类已经为机器学习建立了完整的描述体系，但对人类自身的学习却仍停留在模糊的直觉层面，连“学习是在做什么”“知识是什么”“怎样才算学会”等基本问题，往往只能凭感觉回答，无法准确描述——就如同在物理学出现之前，人们仅凭直觉解释自然现象一样。缺乏精确的描述体系，就难以有效分析和改进学习。本书旨在为学习建立一套系统化框架，仿照物理学描述自然现象、医学描述人体与疾病的方式，对学习的目标、知识类型、材料类型、学习方式、验证手段及失败类型，进行系统的分类与定义。
