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