A Global Map of AI and CS Courses: Which Ones Can You Actually Study in Public?

TL;DR This map audits AI and CS courses at Stanford, CMU, MIT, and UC Berkeley in 2025–2026 using four access labels: A0 for a visible catalog entry, A1 for a public syllabus, A2 for partial materials, and A3 for a self-study-ready package. A course site or YouTube playlist can exist without giving outsiders access to the current videos, assignments, or starter code.

#ai-course #cs-course #learning-path #self-study #open-course

Table of Contents

Public is not yes or no
Tier one: courses you can follow end to end
Tier two: current material with one missing piece
Tier three: a curriculum in transition, or a semester not yet released
How to read the four schools
Where CSDIY fits
Where this series stands
Update log
References
1. Site series articles
2. Official course sites and external resources

Series: Global AI and CS Course Maps (0 / 6)

A Reading Guide to Stanford's CS Courses: Ordered by Prerequisites, from CS106A to CS336

🌏 中文版

Search for Stanford CS229, Berkeley CS188, or MIT deep learning and you will quickly find course sites, YouTube videos, and community notes. The hard question comes next: do those resources belong to the same semester? Do they still open without a university account? Does the assignment link include starter code and required assets, or only a prompt?

This map starts with Stanford, Carnegie Mellon University (CMU), MIT, and UC Berkeley, covering 2025–2026. A complete 2026 edition takes priority. If the new semester has only a schedule or keeps video inside an LMS while an official 2025 edition is materially more complete, the 2025 edition can be the main version. Every course guide names its semester; it will not quietly combine 2025 video with 2026 assignments.

This is not a university ranking. It answers two narrower questions: how each school organizes AI and CS, and how much of that structure an outsider can actually use.

Public is not yes or no

“Public course” can refer to at least seven different things: a catalog description, syllabus, slides, assignment prompts, starter code, solutions, or recordings. Publishing one of them is enough for a search engine to surface the site. It is not enough to let someone complete the course.

This site uses four editorial labels. They are not university ratings; they limit what an article is allowed to promise.

Label	What an outsider gets	What a guide may promise
A0: catalog visible	Title, units, description	Only the course's place in the map
A1: syllabus visible	Syllabus, weekly topics, readings	Scope analysis, not assignment experience
A2: materials partly open	Lectures, some assignments, or video	A topic-focused reading guide with explicit gaps
A3: self-study ready	Systematic materials, assignments, and required assets	A complete self-study path

Video is not required for A3. Complete notes, assignments, starter files, and a clear sequence can be enough. The reverse also holds: a YouTube playlist without practice material does not automatically make a course self-study ready.

Try this: when you find an “open course,” do not press play yet. Spend five minutes locating its syllabus, first assignment, and starter code. Decide whether to invest dozens of hours only after all three checks.

Tier one: courses you can follow end to end

One of the cleanest examples is MIT 6.S191: Introduction to Deep Learning. Its 2026 edition (Mar 30–May 25, now completed) publishes nine videos, slides, and three software labs. The 2025 archive keeps ten videos and three labs. It is highly usable for self-study, with one important qualifier: this is an intensive bootcamp, not a semester-long deep learning course.

Berkeley CS188 Spring 2026 is also close to fully public. Its site exposes slides, textbook chapters, discussion material, six Pacman projects, and lecture videos (archived as of 2026-08-27; current term is su26). Ed, official grades, and staff support remain for enrolled students, but outsiders can still follow the main learning path. The site's CS188 Spring 2026 overview already maps all six projects and a suggested study order.

Stanford is not a collection of isolated recordings either. The Stanford CS course map orders the route from CS106A through CS336 by official prerequisites. CS336 Spring 2026 (Mar 30–Jun 3, now completed) publishes lecture material and five GitHub assignments plus a full YouTube playlist, while Stanford Online carries official Spring 2025 recordings. Its constraint is compute rather than URLs: open materials do not make every assignment free to run.

Courses in this tier are the best candidates for single-course guides because an article can connect what to learn, what to build, and where to begin into one path that works.

Tier two: current material with one missing piece

Berkeley CS288 Spring 2026 publishes slides on post-training, RAG, reasoning, and agents, along with three assignments and a project brief. The missing piece is video. The semester's YouTube playlist exists, but anonymous access returns UNPLAYABLE, matching the course site's Berkeley-login notice. The material supports a reading guide; it does not support the claim that the current recordings are public. The CS288 guide series was written within exactly that boundary.

Berkeley CS285 Spring 2026 publishes slides for twenty-five lectures, five assignments, and GitHub starter code. Current recordings sit in bCourses. The official site also links older public recordings, so a defensible guide should analyze the 2026 material and place historical video in a separate alternative-resources box with the year shown. The CS285 guide series follows that principle.

MIT 6.7960 Fall 2025 publishes a complete schedule, slides, readings, and PyTorch Colabs, while problem sets live in Gradescope and solutions in Canvas. This series labels it A2: enough for a deep reading of course design, not enough to promise the enrolled experience.

Try this: define the result you want before choosing an A2 course. Slides and readings may be sufficient for understanding a topic. Missing prompts, datasets, or graders are a stop signal if your goal is to complete every assignment.

Tier three: a curriculum in transition, or a semester not yet released

CMU is the school where old course numbers are most misleading. The new 07-280 AI & ML I integrates search, machine learning, LLMs, and reinforcement learning, followed by 07-380 AI & ML II. Its official FAQ explains that the sequence replaces 15-281 and 10-315. Spring 2026 materials for 15-281 remain useful, but the retired course no longer represents CMU's current AI trunk. The CMU AI/ML course map lays out a self-study route under the new curriculum, and the transition itself is covered in "CMU's AI core redesign".

As of August 27, 2026, 07-280 Fall 2026 is a full course site: a 24-lecture schedule (Aug 25–Dec 3), lecture slides and notes, Friday recitations, 12 assignments (HW0–HW11 including Building AlexNet / GPT-2 / AlphaZero), and weekly pre-reading checkpoints are all online. The earlier "most materials not yet released" state is over; the site's complete 07-280 guide (24 lectures + 3 stage reviews + completion roadmap) tracks this current edition.

11-785 Introduction to Deep Learning illustrates a different boundary. Spring 2026 (now completed) and Fall 2025 both link official YouTube recordings lecture by lecture, slides are public, and Fall 2026 is also online. Assignments, however, span Autolab, Kaggle, and Piazza. Video access is confirmed; complete self-study still depends on auditing each starter asset.

A future or in-progress semester does not become the “latest public course” merely because its schedule exists. This series waits for the material to appear instead of replacing complete content with a newer year.

How to read the four schools

School	The map's central question	Best current public entry points
Stanford	How do systems and mathematical foundations lead into research-level AI?	CS221, CS336; CS103 / CS107 / CS109 / CS111 / CS161 lecture guides completed
CMU	How does the new 07-280/07-380 sequence connect to ML, DL, NLP, and systems?	07-280 Fall 2026 (24 lectures online), 10-301/601, 11-785 (S26 completed, F26 online)
MIT	How do current numbers, semester sites, and historical OCW editions line up?	6.S191 (2026 completed); 6.7960 as an A2 material guide
Berkeley	How do CS188 and CS189 branch into NLP, RL, and vision?	CS188 (sp26 archived, now su26); material-focused guides to CS288 and CS285
Harvard	How does the CS50 series connect to CS181 / CS182?	Which CS50 AI recordings are reused and how much of CS181 / CS182 is open

Harvard was added as the fifth school map on 2026-08-22; see table above.

A school map and a single-course guide make different promises. Even when every learning asset is locked in an LMS, current catalogs, program requirements, and schedules may still support a course map. That article can explain how to choose courses; it cannot promise that a reader can complete them without enrolling.

Where CSDIY fits

CSDIY is useful for answering which edition a community has actually tried to study. It often preserves historical recordings, assignment experience, and supplementary resources that a university schedule will never mention.

It cannot by itself prove that a course still runs in 2026, that the current link allows anonymous access, or that a third-party video is official or openly licensed. Conversely, a course missing from CSDIY may still have enough official material for self-study.

The series therefore uses two evidence tracks: official sources establish current offerings and access; CSDIY adds historical versions and community experience. Neither replaces the other. How Berkeley CS188 and CMU 15-281 ended up sharing one set of Pacman projects can only be reconstructed from community records — that lineage gets its own article, "The Pacman AI project lineage".

Where this series stands

Five school maps are done: Stanford, CMU, MIT, Berkeley, and Harvard. Single-course guides have expanded significantly:

Berkeley CS188 Spring 2026 overview, with guides covering search, MDPs, Bayes nets, and machine learning (sp26 archived, now su26)
Berkeley CS285 Spring 2026 overview, with guides from imitation learning and policy gradients through offline RL
Berkeley CS288 overview, from foundations and transformers to agents
CMU 10-301/601 overview, walking the whole course through nine assignments
Complete CMU 07-280 course guide: all 24 lectures for Fall 2026, plus a wrap-up with a route to the next course
Complete CMU 11-785 deep learning guide: all 28 lectures, with the incomplete assignment chain flagged (S26 completed, F26 online)
MIT 6.S191 guide: how to actually run the nine lectures and three labs that are fully public (2026 edition Mar 30–May 25 completed)
Stanford CS336 and CS221 also have series: CS336 topic deep dives starting from tokenization (Spring 2026 completed) and a 20-lecture CS221 walkthrough
Stanford foundations lecture series, all with bilingual guides: CS103 28 lectures, CS107 30 lectures, CS109 22 lectures, CS111 28 lectures, CS161 18 lectures

Still unwritten: single-course guides for Harvard's CS181 / CS182; CMU 07-380 (first offered Fall 2026, offered every semester per official FAQ) gets audited once its site goes live. The MIT 6.7960 and Harvard CS50 AI single-course guides are now live on this site in the ai/tech categories (see References).

If you want one course to start tonight, run a small test: open MIT 6.S191's first lab or Berkeley CS188's first project and give yourself ninety minutes. If you can still state what the environment is missing and what you would do next, the course belongs on your learning plan. Saving a playlist is not a start.

Update log

2026-08-27 (3): Added CMU 07-380 Fall 2026 overview (first offering, 26 lectures A2→A3 transition, Lec01 public, compared with 07-280); removed 07-380 from "still unwritten" list.
2026-08-27: Re-audited Spring 2026 as completed: MIT 6.S191 (Mar 30–May 25), Stanford CS336 (Mar 30–Jun 3), Berkeley CS188 sp26 (archived, now su26), and CMU 11-785 S26 (F26 online) now marked as completed; CMU 07-280 Fall 2026 updated from "most materials not yet released" to 24 lectures / 12 assignments / weekly checkpoints all online, matching this site's 07-280 lecture guides; table expanded to five schools and Stanford CS103 / CS107 / CS109 / CS111 / CS161 completion noted; 07-380 now "first offered Fall 2026" per FAQ.
2026-08-26: An audit found that the MIT 6.S191, CMU 11-785, CMU 07-280, CS336, and CS221 guides are already live on this site under the ai category. The "still unwritten" list now correctly names 6.7960, CS189, and Harvard's three courses.
2026-08-26 (earlier): The follow-up articles (four school maps, Harvard, and the CS188 / CS285 / CS288 / 10-301 guide series) are now live. Added inline links throughout, replaced "What comes next" with a current-state list, and restructured References into site articles versus official sources.
2026-08-27 (2): Added Berkeley CS189 Spring 2025 overview (HW1–7 A3, compared with Fall 2026 eecs189.org/fa26 27-lecture calendar); removed CS189 and MIT 6.7960 / Harvard CS50 AI from the "still unwritten" list (the latter two are now live in ai/tech); original 2026-08-27 entry retained.

A Global Map of AI and CS Courses: Which Ones Can You Actually Study in Public?

Public is not yes or no

Tier one: courses you can follow end to end

Tier two: current material with one missing piece

Tier three: a curriculum in transition, or a semester not yet released

How to read the four schools

Where CSDIY fits

Where this series stands

Update log

References

Site series articles

Official course sites and external resources

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