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Medium Post · Courseware Map: Complete Free Resource Guide for Stanford CS Core (12 Courses)

The key to self-study is resources. Stanford offers the best courses, MIT provides courses available for full download, and combining the two creates a completely free guide with no gaps. I've broken down each of the 12 core courses one by one: where to find the official pages, PPTs, videos, textbooks, and MIT equivalent courses—all in one article.

Part 2 · Course Map: All the Free Resources to Complete Stanford’s CS Core in 12 Courses

I. Self-Study Is a Battle of Resources—Resources Determine Life or Death

The previous post covered the coordinate system—why Stanford CS serves as the backbone. This post covers the ammunition: where to get lecture slides, videos, and textbooks for every course, and which ones are free and which require detours.

For self-learners, the ability to access complete course materials, videos, and assignments for free is a matter of life and death. The good news: the combination of Stanford + MIT is the only pair in the world that can assemble a completely comprehensive free resource map. Stanford provides the best courses (with downloadable videos), and MIT provides courses where everything is downloadable.

II. Two Trump Cards First

SEE (Stanford Engineering Everywhere): Stanford’s official open-course platform. Core courses like CS106A/B, CS107, and CS229 have videos and lecture materials available for direct download. URL: https://see.stanford.edu

MIT OCW (MIT OpenCourseWare): MIT’s open courseware platform with an “everything is open” policy—PDF handouts, assignments, and exams are all available for bulk download, with videos accompanying most courses. URL: https://ocw.mit.edu

Usage mantra: when you can’t access Stanford videos, go straight to the corresponding MIT course. The corresponding MIT course number is marked next to each course below.

III. Dissecting the 12 Courses One by One

Organized by learning stage. Each course has four essentials: official page, lecture slides, videos, and textbook, plus the corresponding MIT course.

Group 1: Programming Fundamentals (Learn to Code First)

CS106A Programming Methodologies (Python; switched from Java to Python starting in 2023) — The first programming course, taking you from zero to writing small programs.

  • Official page: https://see.stanford.edu/Course/CS106A
  • Slides: The full-course materials zip on the official page (the old eroberts directory is no longer valid)
  • Videos: Official SEE MP4s, downloadable
  • Textbook: Official interactive textbook Karel the Robot Learns Python
  • MIT equivalent: 6.100A (also Python introduction)

CS106B Programming Abstractions (C++) — Data structures and recursion; trees, graphs, and hash tables are all covered here.

  • Official page: https://see.stanford.edu/Course/CS106B
  • Slides: SEE archive PDFs
  • Videos: YouTube playlist PLFE6E58F856038C69
  • Textbook: Stanford CS106B reader (official handouts) / C++ Primer
  • MIT equivalent: 6.1020 (Software Construction, formerly 6.031/6.005)

Group 2: Mathematical Foundation (Determines How Far You Can Go)

Linear Algebra — There’s no need to replicate Stanford’s math course; just use MIT 18.06 (Professor Strang) directly. Full lectures are available on YouTube, and paired with 3Blue1Brown’s visual linear algebra series, this is the公认 best self-study combination.

CS103 Discrete Mathematics — Proofs, sets, graph theory, and logic—the underlying language of algorithms and AI.

  • Official page: https://cs103.stanford.edu
  • Slides: archived course materials(archived)
  • Videos: Campus-only (not accessible from public internet) → use MIT 6.042J (PDF handouts and assignments are all public, works as a full substitute)
  • Textbook: Discrete Mathematics and Its Applications (Rosen)

CS109 Probability — A prerequisite for machine learning; you can fast-forward if you already have a statistics background.

Group 3: Core Systems (What Separates “Can Code” from “Understands Computers”)

CS107 Computer Organization and Systems — Memory, pointers, assembly, and compilation; understand exactly how your code runs on a machine.

CS111 Operating Systems Principles (formerly CS110, retired in Spring 2022) — Processes, concurrency, and file systems.

Group 4: Algorithms (The Hard-Skill Watershed)

CS161 Algorithm Design and Analysis — Big-O, divide and conquer, dynamic programming, and graph algorithms.

Group 5: AI Track (The Main Battlefield for Monetization)

CS229 Machine Learning — Turns machine learning from black magic into rigorously derivable mathematics.

  • Official page: https://cs229.stanford.edu
  • Slides: Official syllabus page PDF notes (fully public)
  • Videos: SEE official MP4s(downloadable)
  • Textbook: Pattern Recognition and Machine Learning (Bishop)
  • MIT equivalent: 6.036 (introductory) / 6.867 (advanced)

CS224N Natural Language Processing — Using deep learning for language tasks.

CS231N Computer Vision — Using deep learning for image recognition.

Group 6: Electives (Follow Your Interest, Don’t Take Too Many)

CS142 Web Applications: Full-stack introduction, slides at lectures.html, Eloquent JavaScript free online. CS145 Databases: Slides at online handouts corresponding to MIT 6.830.

IV. A Key Reminder: Free Course Materials ≠ Application Credentials

This is a pitfall many people fall into, so let’s address it upfront.

The free course materials above are for building solid knowledge—that’s real skill, and it’s what matters most. But if you follow the plan from the previous post and want to earn a degree from Georgia Tech’s OMSCS after completing the courses, then: free MOOCs carry very little weight in applications. OMSCS is blunt about it—“MOOCs alone are rarely sufficient unless you have a STEM undergraduate degree.”

What can serve as “application credentials” fall into two categories:

  1. GT’s official edX verified certificates: Three courses—Python intro, Java object-oriented programming, and data structures & algorithms—include exams and issue verified certificates, costing approximately 4,000–5,000 RMB.
  2. Credit-bearing formal courses: 2–4 online courses from U.S. community colleges with a grade of B or above.

So the full plan is: free courses for knowledge (this series), certificate courses for credentials (GT edX), and the OMSCS degree as the final goal. These three are not in conflict—they form a relay. Details will unfold in the next post, Roadmap.

V. Closing: The Resource Map Is Ready. The Next Step Is the Route.

The map is in place (previous post), and the ammunition is secured (this post). The final piece of the puzzle remains: which course to start with, how long each phase takes, how to know you’re done, and how to actually land that degree—next post: Roadmap.