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CS 自学指南

CS 自学指南

按学习规划选择计算机课程、数学基础和必备工具。

打开官方课程
难度
见前置条件
形式
文档
语言
中文
规模
135 个课节
内容费用
免费
动手
打开即可学习

第一次学这门课?先完成学前准备,把课程目录搭好。

每节课这样学

  1. 发提示词把下面的每课提示词发给 AI Agent,它会准备这一课的练习文件。
  2. 学官方课节打开对应课节学习,按提示从第一道练习做起;它不会替你写答案。
  3. 记进度学完让 AI Agent 在 progress.md 勾选,并把疑问整理到 questions.md。
每课提示词 · 每次学习前用
继续学习「CS 自学指南」。课程目录是 cs-self-learning-guide/(不知道完整路径就先问我)。
1. 读 progress.md,找到下一课;如果我指定了第几课,就准备那一课。
2. 打开这一课的官方页面和本地讲义,列出本课练习要用的工具、文件和样例数据。缺少的先告诉我,等我同意再安装或下载。
3. 在 lessons/ 对应的课节文件夹里准备好练习:exercises/ 里给每道题建一个文件,写上题号和题目原文链接,不写答案;已有文件保留,样例数据放到 data/。拿不到官方题目或起始代码时记录限制,不编造替代品。
4. 按 README 运行开课时生成的自检脚本或在线检查清单,并检查本课新增工具能否正常使用。
5. 告诉我:本课先读哪几节、从哪道练习开始、大约需要多久。我学完后,帮我在 progress.md 勾选并把问题整理到 questions.md。

边界:
- 需要管理员权限、会产生费用或需要填写密钥的操作,先告诉我原因,等我同意。密钥由我在本机配置,不发到聊天,不写进笔记或版本库;原项目需要 .env 时确认已被 Git 忽略。
- 不替我写练习或作业答案,也不替我提交。我卡住时只给提示;我明确要求时才给解答。
- 只下载许可允许个人保存的材料;其余只放链接。不把未运行的命令说成成功。

课节共 135 节 · 官方顺序

  1. 01前言
  2. 02如何使用这本书
  3. 03一个仅供参考的CS学习规划
  4. 04翻墙
  5. 05Vim
  6. 06Emacs
  7. 07Git
  8. 08GitHub
  9. 09GNU Make
  10. 10CMake
  11. 11LaTeX
  12. 12Docker
展开其余 123 节
  1. 13Scoop
  2. 14日常学习工作流
  3. 15实用工具箱
  4. 16毕业论文
  5. 17信息检索
  6. 18好书推荐
  7. 19MIT18.01/18.02: Calculus
  8. 20MIT18.06: Linear Algebra
  9. 21MIT6.050J: Information theory and Entropy
  10. 22UCB CS70: discrete Math and probability theory
  11. 23UCB CS126: probability theory
  12. 24MIT 6.042J: Mathematics for Computer Science
  13. 25MIT18.330: Introduction to numerical analysis
  14. 26Standford EE364A: Convex Optimization
  15. 27The Information Theory, Pattern Recognition, and Neural Networks
  16. 28MIT-Missing-Semester
  17. 29Sysadmin DeCal
  18. 30UCB CS61A: Structure and Interpretation of Computer Programs
  19. 31CS50P: CS50's Introduction to Programming with Python
  20. 32MIT6.100L: Introduction to CS and Programming using Python
  21. 33Harvard CS50: This is CS50x
  22. 34Duke University: Introductory C Programming Specialization
  23. 35AmirKabir University of Technology AP1400-2: Advanced Programming
  24. 36Stanford CS106L: Standard C++ Programming
  25. 37Stanford CS106B/X
  26. 38MIT 6.092: Introduction To Programming In Java
  27. 39Stanford CS110L: Safety in Systems Programming
  28. 40KAIST CS220: Programming Principles
  29. 41KAIST CS431: Concurrent Programming
  30. 42Cornell CS3110: OCaml Programming Correct + Efficient + Beautiful
  31. 43Haskell MOOC
  32. 44EE16A&B: Designing Information Devices and Systems I&II
  33. 45UCB EE120 : Signal and Systems
  34. 46MIT 6.007 Signals and Systems
  35. 47UCB CS61B: Data Structures and Algorithms
  36. 48Coursera: Algorithms I & II
  37. 49MIT 6.006: Introduction to Algorithms
  38. 50MIT 6.046: Design and Analysis of Algorithms
  39. 51UCB CS170: Efficient Algorithms and Intractable Problems
  40. 52MIT 6.031: Software Construction
  41. 53UCB CS169: software engineering
  42. 54CMU 17-803: Empirical Methods
  43. 55CMU 15-213: CSAPP
  44. 56Stanford CS110: Principles of Computer Systems
  45. 57Coursera: Nand2Tetris
  46. 58UCB CS61C: Great Ideas in Computer Architecture
  47. 59ETHz: Digital Design and Computer Architecture
  48. 60ETHz: Computer Architecture
  49. 61MIT 6.1810 (6.S081): Operating System Engineering
  50. 62UCB CS162: Operating Systems and Systems Programming
  51. 63NJU OS: Operating System Design and Implementation
  52. 64HIT OS: Operating System
  53. 65CMU 15-418/Stanford CS149: Parallel Computing
  54. 66MIT 6.5840 (6.824): Distributed System
  55. 67UCB CS161: Computer Security
  56. 68MIT 6.1600: Foundations of Computer Security
  57. 69MIT 6.858: Computer System Security
  58. 70ASU CSE365: Introduction to Cybersecurity
  59. 71ASU CSE466: Computer Systems Security
  60. 72SU SEED Labs
  61. 73UCB CS168: Introduction to the Internet: Architecture and Protocols
  62. 74Stanford CS144: Computer Network
  63. 75USTC Computer Networking:A Top-Down Approach
  64. 76Computer Networking: A Top-Down Approach
  65. 77UCB CS186: Introduction to Database System
  66. 78CMU 15-445: Database Systems
  67. 79Caltech CS122: Database System Implementation
  68. 80Stanford CS346: Database System Implementation
  69. 81CMU 15-799: Special Topics in Database Systems
  70. 82PKU 编译原理实践
  71. 83Stanford CS143: Compilers
  72. 84NJU 编译原理
  73. 85KAIST CS420: Compiler Design
  74. 86USTC 编译原理与技术
  75. 87SJTU 编译原理
  76. 88Stanford CS242: Programming Languages
  77. 89NJU 软件分析
  78. 90PKU 软件分析
  79. 91Cambridge: Semantics of Programming Languages
  80. 92GAMES101
  81. 93GAMES202
  82. 94GAMES103
  83. 95Stanford CS148
  84. 96CMU 15-462
  85. 97USTC CG
  86. 98MIT web development course
  87. 99Stanford CS142: Web Applications
  88. 100University of Helsinki: Full Stack open 2022
  89. 101CS571 Building UI (React & React Native)
  90. 102UCB Data100: Principles and Techniques of Data Science
  91. 103Neural Networks: Zero to Hero
  92. 104Harvard CS50's Introduction to AI with Python
  93. 105UCB CS188: Introduction to Artificial Intelligence
  94. 106Coursera: Machine Learning
  95. 107Stanford CS229: Machine Learning
  96. 108UCB CS189: Introduction to Machine Learning
  97. 109CMU 15-442/642: Machine Learning Systems
  98. 110智能计算系统
  99. 111CMU 10-414/714: Deep Learning Systems
  100. 112MIT6.5940: TinyML and Efficient Deep Learning Computing
  101. 113Machine Learning Compilation
  102. 114UCSD CSE234: Data Systems for Machine Learning
  103. 115Coursera: Deep Learning
  104. 116国立台湾大学: 李宏毅机器学习
  105. 117CMU 11-785: Introduction to Deep Learning
  106. 118MIT 6.7960: Deep Learning
  107. 119NYU DLSP21: NYU Deep Learning Spring 2021
  108. 120UMich EECS 498-007 / 598-005: Deep Learning for Computer Vision
  109. 121Stanford CS231n: CNN for Visual Recognition
  110. 122Stanford CS224n: Natural Language Processing
  111. 123Stanford CS224w: Machine Learning with Graphs
  112. 124UCB CS285: Deep Reinforcement Learning
  113. 125学习路线图
  114. 126MIT 6.S184: Generative AI with Stochastic Differential Equations
  115. 127CMU 11-868: Large Language Model System
  116. 128CMU 11-667: Large Language Models: Methods and Applications
  117. 129CMU 11-711: Advanced Natural Language Processing
  118. 130学习路线图
  119. 131CMU 10-708: Probabilistic Graphical Models
  120. 132Columbia STAT 8201: Deep Generative Models
  121. 133U Toronto STA 4273 Winter 2021: Minimizing Expectations
  122. 134Stanford STATS214 / CS229M: Machine Learning Theory
  123. 135后记

来源与使用须知

原始资源
CS 自学指南 收录版本:原站当前版本(2026-10-10 核对)
收录时间
本站首次收录 2026-10-10

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  • 本站只提供原始链接和学习指南,不转载、不镜像课程材料;引用时请注明原作者与出处。