Series finale: Retrospecting timeless core from 7 weeks/12 projects, gaps in 2020/2023 recordings vs 2026 reality, free OCW route completeness, and forward roadmap (Transformers, LLM fine-tuning, RAG, Agents, Evaluation).
CS181 2026 is A3 with hw0–6 as the weekly clock (no public recordings); 2025 adds a practical, 2024 has two midterms, 2023 was taught by Weiwei Pan. Start with HW0, then follow hw1→hw6.
CS50 AI's OpenCourseWare edition publishes seven weeks of lectures, slides, notes, and twelve Python projects with autograder feedback, plus a free CS50 Certificate if you score at least 70% on every project. The catch: weeks 0–5 still use the Spring 2020 recordings; only Week 6 (Language) was re-recorded, in 2023.
You don't need to become a researcher to understand AI models systematically. This series starts from what you can see (tokens, context windows) and works up to self-hosting open-source models — 18 articles covering everything you need to choose models, read benchmarks, and estimate costs.
Finishing 07-280 means more than reading 24 guides: produce a search engine, supervised-model comparison, CNN/GPT-2 experiments, and a small RL-plus-MCTS system before choosing 07-380, 10-301, or a specialist course.
Lectures 26–28 close with nuclear monitoring, AI safety, and reflection. Independent completion should preserve assumptions, test evidence, and failure analysis for Projects 1–5 instead of reporting only autograder scores.
Spring 2025 at people.eecs.berkeley.edu/~jrs/189s25 is the only A3 self-study edition with notes, videos, HW1–7, code/data and past exams; Fall 2026 at eecs189.org/fa26 has a 27-lecture schedule but most materials are not yet released and the rotating site can 404 old URLs.
07-380 Fall 2026 is the first offering of CMU's new AI II, 26 lectures from logic, planning and optimization to probabilistic graphs and generative systems; Lec01 and Prop Logic are public, HW1-7, six quizzes and the final project are still TBD — an A2→A3 transition with the 07-280 bridge.
CS50 AI is Harvard's most complete public entry point, but the Summer 2026 course still uses 2020 recordings and assignment assets while the rolling OCW projects have moved to other editions. CS181 Spring 2026 exposes current homework and notes without current recordings; CS182 Fall 2026 has not yet completed an offering.
CS107 Lecture 26 closes ten weeks through six big questions: representation, text, memory, generics, execution, and allocation. It checks the learning goals through the explicit allocator and points toward CS111 and other systems courses.
Berkeley has no standalone undergraduate AI degree. A workable path builds on the CS BA or EECS BS foundation, enters through either CS188's broad AI curriculum or CS189's mathematical machine learning curriculum, then branches into deep learning, NLP, vision, or reinforcement learning. Many 2025–2026 courses are A3, but the newest class, the newest stable URL, and the best self-study edition are not always the same.
Stanford has no AI degree; AI is a track inside CS. CMU launched the first U.S. B.S. in Artificial Intelligence in 2018, divided AI into four clusters, required one course from each, and made ethics a graduation requirement. At the master's level, MSAII sits not in CS but in the Language Technologies Institute; 84 of its 195 units cover an innovation process ending in a fundable capstone. Two official-page conflicts emerged during verification: whether the AI Core has two or three courses, and whether MSAII totals 192 or 195 units.
CMU's current BSAI now runs through 07-280 and 07-380 before branching into an NLP/vision core and four AI clusters, but 07-380 does not debut until Fall 2026. The residual Spring 2026 materials for 07-280 and the complete 10-301/601 site already support self-study; retired 15-281 remains a useful legacy route.
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.
MIT has offered Course 6-4, a formal BS in Artificial Intelligence and Decision Making, since 2022. For an outside learner, however, the current degree requirements, the 2025–2026 course sites, and the best OCW editions rarely line up. A workable route follows 6-4's programming, algorithms, linear algebra, and probability foundation, then selects among 6.S191, 6.3900, 6.4110, 6.7960, vision, and robotics according to what is actually public.
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.
Every official course platform from OpenAI, Anthropic, and Google, plus Stanford CS146S/CS336, Elements of AI, Hugging Face, MIT 6.S191 and more — scraped page by page, then re-sorted into four tiers: AI-curious, vibe coding, shipping to production, and how models actually work. Also covers self-study repos still being updated in 2026 and browser-based platforms that need no local setup, filtered by last-commit date rather than star count. The conclusion: nearly all of it is free. What is scarce is not courses, it is the judgment to pick one. And tier four will not fix your tier three problem.