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CS124 is the first course in Stanford's NLP track: ten weeks from tokenization, n-grams, text classification, information retrieval and embeddings to LLMs and Transformers. The textbook is the free Speech and Language Processing, and the nine assignment repos plus the lab problems and solutions are public; the pre-recorded videos, quizzes and autograding need a Stanford account.
CS221 is Stanford's course on AI principles and techniques. It lays AI out in four layers — reflex, states, variables and logic — with deep learning as only the lowest one. The executable Python lectures and all eight assignments, with code skeletons and a local grader, are public; lecture recordings, hidden test cases and assignment solutions are not.
CS224N is Stanford's course on natural language processing with deep learning: it goes from word vectors and backpropagation to Transformers, every later lecture builds on Transformers, and the four assignments include writing a decoder-only Transformer yourself. The current slides and assignments are public, and Assignment 3 ships unit tests that run on a laptop; the complete public recordings come from an earlier offering and no longer match the current syllabus.
CS224U is Stanford's natural language understanding course, the follow-on to CS224N: it covers domain adaptation for sentiment analysis, retrieval-augmented in-context learning, advanced behavioral evaluation and analysis methods, with a bake-off attached to each of the three assignments. The material is a public GitHub repo, and the slides and the full recordings of the online version are public too; the data loading in the first assignment needs a fix before it runs in a fresh environment.
CS224V is Stanford's Agentic AI course: it teaches how to constrain LLM agents with formal semantics, knowledge graphs and formal methods so they fabricate less, with two assignments and then a project that carries most of the grade. The fourteen lecture decks, the reading list, the assignment handouts and the open-source systems the course uses (STORM, WikiChat, SUQL, Genie Worksheets) are public; there are no public recordings, the posted slides are deliberately incomplete, and the credentials the assignments need go only to enrolled students.
CS224W is Stanford's machine learning with graphs course: nineteen lectures from node embeddings, GNNs, graph transformers and knowledge graphs to relational deep learning and GNNs with LLMs. The slides, three written homeworks and six Colabs are public and run as downloaded; the public recordings date from 2021, so the newer topics in the second half exist only as slides, and the exam and autograding are out of reach.
CS228 is Stanford's probabilistic graphical models course, in three parts — representation, inference and learning: graphs write down independence assumptions so that exponentially large probability computations become polynomial. What self-learners can get is the 16-chapter public notes, cs228-notes; lecture slides, recordings and the current assignments are not available.
CS229 is the Stanford course that works through the mathematical derivations of machine learning. The Spring 2026 recordings make 17 lectures public: the first ten cover linear models, neural networks, and unsupervised learning, and the last seven go on to diffusion models, LLMs, and training reasoning models with reinforcement learning. This series has one post per lecture, and this post is the map: what each lecture actually teaches, where the video titles don't match, and where to pick up what the lecture notes cover but the lectures skipped.
CS25 is Stanford's 1-unit seminar where attendance is the only homework and anyone can audit. Of the nine talks in the Spring 2026 season, the three worth your time are Albert Gu on the inductive biases of SSMs vs Transformers, Charles Frye on serving inference across thousands of GPUs, and Victoria Lin on what native multimodality still hasn't solved.
CS329Z is Stanford's course on engineering AI agents: you first build RAG, tool use and the agent loop by hand without frameworks, then see what frameworks like DSPy abstract away; the two assignments are building an agent harness and designing an agent evaluation. The syllabus, the assigned readings, the first eleven slide decks, and the starter code, data and public tests for Assignment 1 are available; the recordings require an enrolled Canvas login.
CS336 is Stanford's course on building a language model from scratch: five assignments cover the tokenizer and Transformer, training-systems optimization, scaling laws, data processing, and alignment and reasoning RL, with no code skeleton, only tests. The lectures, recordings, all five assignment repos and the leaderboards are public; from Assignment 2 on you supply your own compute, and the course-sponsored compute and grading are not available.
Tavily exposes Search, Extract, Map, and Crawl through one web API for agents. The free plan includes 1,000 credits per month; basic, fast, and ultra-fast Search cost 1 credit each, while advanced costs 2.
vLLM is the de facto standard for self-hosted LLM inference (89,470 GitHub stars, verified 2026-08-21), built on managing the KV cache the way an OS manages paged memory. But the selection question isn't how fast it is — it's your GPU utilization. Using Red Hat's measured 793 output tokens/second, a fully saturated A100 costs roughly $0.70 per million output tokens; at 10% utilization that becomes $7, more than most cloud APIs.
A web retrieval benchmark must evaluate complete tasks, not HTTP 200s: 30 fixed cases across five failure strata and three live channels, measuring answers, citations, freshness, latency, cost, and unnecessary escalation. This article delivers the harness and gates, but no fabricated ranking while the three live channels remain unconfigured.
An agent should not open a browser for every web task: route first to Search or Fetch, then escalate on explicit signals such as status codes, weak content, JavaScript shells, authentication, or challenge pages, with retry, budget, cache, deduplication, and provenance constraints at every step.
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. 07-380 debuted in Fall 2026, and the same semester launched a graduate-level 11-768 AI Agents course. 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.
AI Engineer reached 600,000 YouTube subscribers in under three years not because it mastered video production, but because it barely needs to produce videos at all: recordings from eight conferences a year create an inexhaustible supply of YouTube material. The real constraint on content creation is structure, not skill.
This map audits AI and CS courses at Stanford, CMU, MIT, UC Berkeley, Harvard, and National Taiwan University (NTU) 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.