A map of Stanford CS core courses, from the degree foundations through AI, NLP, graph learning, and agents, with versioned course guides and prerequisites.
Stanford CS rests on CS103, CS107, CS109, CS111, and CS161; CS221 names three of those plus CS106B as preparation. This guide combines official prerequisites with an explicitly editorial reading order and marks public-material and offering risks.
CS103 teaches you how to write proofs, then teaches you what can't be proven — but the part nobody mentions is that it ships C++ programming assignments, starting with PS0: install Qt Creator. Its real asset is a shelf of homegrown 'Guide to X' handouts and a Proofwriting Checklist that graders actually deduct points against, all public. Solutions and practice exams sit behind Stanford login, and the Honor Code page explains why.
CS107 runs from Unix and C all the way to x86-64 and writing your own malloc, across seven assignments. But line up four archived syllabi and the course stops looking like one course: assignments are worth 40% in three quarters and 20% in Summer 2026, where in-class quizzes take 40%. The resubmission policy exists only in the quarters Cain taught; Troccoli's quarter has none. The one assignment that accepts no late days is the final heap allocator. And what blocks a self-learner isn't the autograder — it's that every starter repo lives on AFS.
Every lecture in CS109's Summer 2026 offering ships with an official LLM Learning Guide — six concepts, a Learn prompt and a Test me prompt for each, written week by week across the quarter for a total of 23 PDFs. The same course's honor code Rule 4 forbids asking an LLM to solve your homework, and 65% of the grade sits in proctored exam rooms. Those two facts are halves of one design.
CS111's nine assignments run from lambdas to crash recovery in a journaling file system. Reading the site page by page turns up three things the syllabus blurb never mentions: assignment 3 is the point of no return, because assignment 4 compiles your assignment 3 code; a whole block of the final exam asks for definitions of ethics terms, and the public practice sheet ships with answers; and pasting your own code into an AI tool to ask about it is written down, in plain words, as an Honor Code violation.
The first slide of CS161 names three goals: design, analysis, communication. The third one is why handwritten homework scores zero and why solutions have to read like a memo to a colleague. Of the eight problem sets, HW2 is the wall. The lecture notebooks exist to show that timing runs can't tell you which algorithm is faster. And the summer offering is a completely different course wearing the same number.
CS221 lays AI out along one axis, and reflex models — deep learning — sit in the lowest slot, with states, variables and logic above them. When Percy Liang took over in Autumn 2025 he replaced the slides with runnable Python and wrote 'Cut constraint satisfaction problems :(' into the source of the first lecture — yet ExploreCourses and Stanford Online both still advertise constraint satisfaction as a course topic. The project has gone from 20% of the grade in 2019 to extra credit only.
CS124 is the first course in Stanford's NLP branch. Its textbook is Jurafsky's own Speech and Language Processing, free online, and all nine assignment repos are public. But a banner sits on the course homepage: it will not be taught at all in AY 2026–27. And the chapter numbers the syllabus points at no longer match the August 2026 textbook.
The three things you need to self-study CS229 run on three different clocks. The lecture notes are 278 pages and were recompiled in August 2026. The newest problem sets you can download are from summer 2020. The self-assessment Stanford Online tells you to attempt before enrolling is a PDF created in 2008. Seventeen lectures from spring 2026 are public, and the last three are mislabeled.
CS230's first lecture is a course overview, but Andrew Ng spends most of it on three things: why scaling works, when prompting stops being enough, and why he thinks 'don't learn to code' is one of the worst pieces of career advice ever given.
CS228's official prerequisite is a single line — 'basic probability theory and algorithm design and analysis' — with no named course. But ExploreCourses shows it was last offered in Winter 2024, and the next slot, Winter 2027, still has a blank instructor field. What a self-learner can actually get is cs228-notes: 16 chapters, complete, last touched in June 2025.
CS224N has kept every course website since 2000 online. In Winter 2019, Transformers were lecture 14, taught by a guest. In Winter 2026 they are lecture 5, and every lecture after that assumes you already know them. The machine translation assignment is gone; assignment 3 now has you code a decoder-only Transformer from scratch, with pytest suites that run on your laptop.
CS224U's teaching material isn't a slide deck — it's an Apache-2.0 GitHub repo holding the lecture notebooks, all three assignments, and the grading document for the final project. But the on-campus course has skipped three straight academic years since Spring 2023, and ExploreCourses has it back on the books for Spring 2026-27. The official description still lists relation extraction and semantic parsing; the 2023 syllabus covers neither. And the data-loading cell in the first assignment breaks in a fresh environment today, on a Hugging Face compatibility change.
CS224V only became Agentic AI in the 2026–2027 catalog, and the rename changed nothing underneath: the course still translates natural language into formal semantics and constrains agents with SMT solvers and knowledge graphs instead of wiring frameworks together. Seven of the eleven mandatory readings come out of the instructor's own lab. Every slide deck is public, and the course site says outright that they are deliberately incomplete.
All six CS224W Colabs download and run today, and the first one needs only NetworkX — no PyG install at all. But the exam is 35% of the grade, the largest single piece, and it's an in-person closed-book sitting. The public recordings stop at 2021 and cover none of the current syllabus's second half: graph transformers, relational deep learning, LLM+GNN.
CS329Z is a new three-unit agent engineering course debuting at Stanford in Autumn 2026. Its first homework asks you to build RAG, tool calling and a ReAct loop from scratch with litellm, then rewrite the same components in DSPy and hand in the comparison. The course site lives in a public GitHub repo, and the commit log shows the assignment count dropping from three to two in mid-August — the one that got cut was 'Data for Agents'.
Of the seventeen regular CS336 lectures, only nine are executable Python programs; the other eight are PDF slide decks — and the split falls exactly along the two instructors. Assignment 1's handout carries eight 'Low-Resource Tips' for finishing it on a laptop. Assignments 2 through 5 carry none. The course page lists the hourly price of a B200; the handouts list how many B200 hours each problem needs.
CS329A is built around the generation–verification gap: models can produce the right answer but can't tell which one it is. The conclusion the course draws about itself matters more — today's methods make models more consistent, not smarter. Nine lectures are public, out of twenty.
Stanford CS146S's Fall 2026 syllabus compresses prompting from a full week into a single bullet, drops the terminal and UI-generation weeks, and adds Agent Skills, Agent-Ready Codebases, Background Agents, and AI-Native Team. Grading moved too: the final project fell from 80% to 50%, with 30% now on open source contributions. This series reads all ten weeks.