A bilingual learning guide for computer science.
这是一份双语计算机科学学习指南。
- Full index / 全站索引: Website / 网站 · Source / 源文件
Start with the introduction if you are deciding whether this roadmap is useful for you. Then use the chapter links as a map rather than a strict checklist: foundations first, core areas next, and applied or interdisciplinary topics when they match your goals.
如果还在判断这份路线图是否适合自己，可以先读导论。后面的章节更适合作为地图，而不是必须逐项完成的清单：先打基础，再进入核心领域，应用和跨学科部分可以按兴趣与目标选择。
Start here if you are wondering whether this roadmap is for you. These questions handle the ordinary anxieties before study begins: what background is enough, how long the road may take, how to use AI without letting it replace your own thinking, and how to read the rest of the guide without treating it as a checklist.
如果你还在犹豫该不该开始，先读这一章。这里回答的不是技术细节，而是学习前最常见的顾虑：背景够不够、要花多久、这份指南怎么用、AI 能不能帮忙，以及怎样把后面的章节当作路线图而不是压力清单。
- Q&A: Can I learn computer science? / 问答：我能学会计算机科学吗？ (Source / 源文件)
- History of Computing / 计算机发展史 (Source / 源文件)
Use this chapter to build the toolkit you will keep reusing. You do not need to become a mathematician first; aim to write small programs, read basic proofs, calculate with vectors and probability, and recognize common algorithms so later chapters have something to attach to.
这一章是在准备以后会反复用到的工具。你不需要先变成数学家；目标是能写小程序、看懂基本证明、会用向量和概率思考问题，并能认出常见算法，这样后面的系统、AI 和理论内容才有落点。
| No. | Topic / 主题 | Type / 定位 | Source / 源文件 |
|---|---|---|---|
| 2.1 | Programming as Conceptual Practice / 作为概念实践的编程 | Base / 基础 | MD |
| 2.2 | Discrete Mathematics and the Foundations of Proof / 离散数学与证明基础 | Base / 基础 | MD |
| 2.3 | Linear Algebra / 线性代数 | Base / 基础 | MD |
| 2.4 | Calculus and Mathematical Analysis / 微积分与数学分析 | Base / 基础 | MD |
| 2.5 | Probability and Statistics / 概率与统计 | Base / 基础 | MD |
| 2.6 | Algorithms and Data Structures / 算法与数据结构 | Core / 核心 | MD |
Come here when you start asking why programs work at all, what problems can be solved, and why some things stay hard no matter how fast computers get. Read it slowly; the goal is not to collect names, but to learn the style of thinking behind proofs, languages, and limits.
当你开始问“程序为什么能工作”“哪些问题根本算不出来”“为什么有些问题再快的机器也很难”时，就进入这一章。它适合慢慢读，重点不是记术语，而是学会用证明、语言和边界来思考计算。
| No. | Topic / 主题 | Type / 定位 | Source / 源文件 |
|---|---|---|---|
| 3.1 | Logic in Computer Science / 计算机科学中的逻辑 | Base / 基础 | MD |
| 3.2 | Theory of Computation / 计算理论 | Core / 核心 | MD |
| 3.3 | Complexity Theory / 复杂性理论 | Core / 核心 | MD |
| 3.4 | Programming Language Theory and Formal Semantics / 程序语言理论与形式语义 | Core / 核心 | MD |
| 3.5 | Type Theory / 类型论 | Advanced / 进阶 | MD |
| 3.6 | Formal Methods and Proof Assistants / 形式化方法与证明助手 | Advanced / 进阶 | MD |
| 3.7 | Category Theory in Computer Science / 计算机科学中的范畴论 | Advanced / 进阶 | MD |
| 3.8 | Information Theory / 信息论 | Advanced / 进阶 | MD |
This chapter makes the computer less mysterious. It follows code down into hardware, operating systems, networks, storage, and online services, so problems like slowness, crashes, data loss, and security failures start to have causes you can reason about.
这一章会把“电脑怎么运行”变得不那么神秘。它把代码一路带到硬件、操作系统、网络、存储和线上服务，让卡顿、崩溃、数据丢失和安全问题不再只是现象，而是可以分析的原因。
| No. | Topic / 主题 | Type / 定位 | Source / 源文件 |
|---|---|---|---|
| 4.1 | Computer Organization and Architecture / 计算机组成与体系结构 | Base / 基础 | MD |
| 4.2 | Operating Systems / 操作系统 | Core / 核心 | MD |
| 4.3 | Computer Networks / 计算机网络 | Core / 核心 | MD |
| 4.4 | Databases / 数据库 | Core / 核心 | MD |
| 4.5 | Security and Cryptography / 安全与密码学 | Advanced / 进阶 | MD |
| 4.6 | Distributed Systems / 分布式系统 | Advanced / 进阶 | MD |
| 4.7 | Compilers and Language Runtimes / 编译器与语言运行时 | Advanced / 进阶 | MD |
| 4.8 | Stream Processing and Event-Driven Architectures / 流处理与事件驱动架构 | Practice / 实践 | MD |
| 4.9 | Container Orchestration and Cloud-Native Infrastructure / 容器编排与云原生基础设施 | Practice / 实践 | MD |
| 4.10 | Observability and Reliability Engineering / 可观测性与可靠性工程 | Practice / 实践 | MD |
| 4.11 | High-Performance Computing / 高性能计算 | Advanced / 进阶 | MD |
This chapter gives you a map of AI without asking you to chase every new model. Start with how machines learn from data, then use deep learning and large language models as concrete examples; the later topics help you understand power, failure, and responsibility.
这一章给 AI 一个清晰地图，而不是追逐每一个新模型。先理解机器怎样从数据中学习，再把深度学习和大语言模型当作具体例子；后面的主题帮助你理解 AI 的能力、失败方式和责任问题。
| No. | Topic / 主题 | Type / 定位 | Source / 源文件 |
|---|---|---|---|
| 5.1 | Classical AI and Symbolic Approaches / 经典 AI 与符号方法 | Base / 基础 | MD |
| 5.2 | Machine Learning Foundations / 机器学习基础 | Base / 基础 | MD |
| 5.3 | Deep Learning / 深度学习 | Core / 核心 | MD |
| 5.4 | Reinforcement Learning / 强化学习 | Advanced / 进阶 | MD |
| 5.5 | Large Language Models and Foundation Models / 大语言模型与基础模型 | Applied / 应用 | MD |
| 5.6 | Interpretability and Mechanistic Interpretability / 可解释性与机制可解释性 | Advanced / 进阶 | MD |
| 5.7 | AI Safety and Alignment / AI 安全与对齐 | Advanced / 进阶 | MD |
This chapter shows how computer science changes shape when it meets real domains. Graphics, interfaces, data, robotics, simulation, quantum computing, and web apps each reward different tradeoffs, so read these as possible directions rather than required steps.
这一章展示计算机科学进入具体领域后会变成什么样。图形、交互、数据、机器人、仿真、量子计算和 Web 应用都有不同取舍，所以更适合作为未来方向来读，不必一开始全都学完。
| No. | Topic / 主题 | Type / 定位 | Source / 源文件 |
|---|---|---|---|
| 6.1 | Computer Graphics and Visual Computing / 计算机图形学与视觉计算 | Applied / 应用 | MD |
| 6.2 | Human-Computer Interaction and Design / 人机交互与设计 | Applied / 应用 | MD |
| 6.3 | Data Engineering and Data Science / 数据工程与数据科学 | Applied / 应用 | MD |
| 6.4 | Robotics and Autonomous Systems / 机器人与自主系统 | Applied / 应用 | MD |
| 6.5 | Scientific Computing and Simulation / 科学计算与仿真 | Applied / 应用 | MD |
| 6.6 | Quantum Computing / 量子计算 | Advanced / 进阶 | MD |
| 6.7 | Web and Application Development / Web 与应用开发 | Applied / 应用 | MD |
This chapter is about what happens after code leaves your own laptop. Software has to be understood, tested, changed, and shipped by teams while requirements, people, and systems keep moving; these topics explain how to keep it maintainable in that reality.
这一章讨论代码离开个人电脑之后会发生什么。软件需要被团队理解、测试、修改和发布；需求、人员和系统都会变化，所以这里关注的是怎样让软件在真实环境里继续可维护。
| No. | Topic / 主题 | Type / 定位 | Source / 源文件 |
|---|---|---|---|
| 7.1 | Software Architecture / 软件架构 | Core / 核心 | MD |
| 7.2 | Software Testing and Quality Assurance / 软件测试与质量保证 | Practice / 实践 | MD |
| 7.3 | Software Process: Delivery, Iteration, and Coordination / 软件过程：交付、迭代与协调 | Practice / 实践 | MD |
| 7.4 | DevOps, Operability, and Continuous Delivery / DevOps、可运维性与持续交付 | Practice / 实践 | MD |
| 7.5 | Technical Debt, Refactoring, and Software Economics / 技术债、重构与软件经济学 | Practice / 实践 | MD |
| 7.6 | Engineering Management and Organizational Design / 工程管理与组织设计 | Practice / 实践 | MD |
This chapter is about everyday working habits. They may look smaller than theory, but they decide whether you can learn from real code, use tools well, collaborate with other people, explain changes clearly, and keep moving when you get stuck.
这一章是日常工作习惯的部分。它们看起来不如理论宏大，却决定你能不能读懂真实代码、用好工具、和别人协作、清楚表达修改，并在卡住时继续前进。
| No. | Topic / 主题 | Type / 定位 | Source / 源文件 |
|---|---|---|---|
| 8.1 | Programming Languages as Practitioner Tools / 作为从业工具的编程语言 | Base / 基础 | MD |
| 8.2 | Development Environment and Tooling / 开发环境与工具 | Practice / 实践 | MD |
| 8.3 | Reading Code / 阅读代码 | Practice / 实践 | MD |
| 8.4 | Writing Code and Software Craftsmanship / 编写代码与软件工艺 | Practice / 实践 | MD |
| 8.5 | Collaboration: Version Control and Code Review / 协作：版本控制与代码审查 | Practice / 实践 | MD |
| 8.6 | Debugging as Methodological Discipline / 作为方法论纪律的调试 | Practice / 实践 | MD |
| 8.7 | Reading Technical Literature / 阅读技术文献 | Practice / 实践 | MD |
| 8.8 | Technical Writing / 技术写作 | Practice / 实践 | MD |
| 8.9 | Career Trajectories and Professional Practice / 职业路径与专业实践 | Practice / 实践 | MD |
Use this final chapter to widen the view after you have some footing. It shows where computer science borrows ideas, creates new questions, and collides with human fields; treat it as a set of bridges, not another list of prerequisites.
最后这一章用来打开视野。等你对计算机科学有了一点站稳的地方，再看它怎样向数学、物理、语言、经济、法律、艺术和哲学借东西、提问题、发生冲突；把它当作桥梁，而不是新的必修清单。
| No. | Topic / 主题 | Type / 定位 | Source / 源文件 |
|---|---|---|---|
| 9.1 | CS and Mathematics: Debt, Difference, and Dialogue / 计算机科学与数学：债务、差异与对话 | Cross / 跨界 | MD |
| 9.2 | CS and Physics: Information, Entropy, and Computation / 计算机科学与物理：信息、熵与计算 | Cross / 跨界 | MD |
| 9.3 | CS, Cognitive Science, and Neuroscience / 计算机科学、认知科学与神经科学 | Cross / 跨界 | MD |
| 9.4 | CS and Linguistics / 计算机科学与语言学 | Cross / 跨界 | MD |
| 9.5 | CS and Economics / 计算机科学与经济学 | Cross / 跨界 | MD |
| 9.6 | CS, Law, and Policy / 计算机科学、法律与政策 | Cross / 跨界 | MD |
| 9.7 | CS and Biology / 计算机科学与生物学 | Cross / 跨界 | MD |
| 9.8 | CS and Art and Design / 计算机科学与艺术设计 | Cross / 跨界 | MD |
| 9.9 | CS and Philosophy / 计算机科学与哲学 | Cross / 跨界 | MD |
These appendix essays are career-field guides and reference materials. They are not prerequisites for the main roadmap; use them when you want to compare directions, understand industry roles, connect core chapters to concrete jobs, or stabilize terminology across the guide.
这些附录是职业领域导览和参考材料，不是主线学习的前置要求。适合在比较方向、理解岗位分工、把主线章节和具体职业连接起来，或统一全书术语时阅读。
Version: v1-2026-7-11
This is a first edition before proofreading. Much of the guide was reconstructed with AI assistance from the original course structure, conception, and selected resources.
当前版本是等待校对的初版。内容大部分在原教程组织方式、原始构想和部分资源基础上，经 AI 辅助重构而成。