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.
MIT 6.7960 Deep Learning (Fall 2025) publishes all 21 lecture decks as public Dropbox PDFs, and most required readings map to free textbook chapters; but the five problem sets are released only through Gradescope, and solutions plus recordings live behind Canvas login. This guide covers how the three instructors split the course, a topic map of all 21 lectures, textbook-based substitutes for lectures, and where outside self-learners realistically stop.
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.
07-280 is CMU's new Spring 2026 AI+ML core: 24 lectures and 12 main assignments move from heuristic search and CSPs to AlexNet, GPT-2, and AlphaZero. Its public material supports self-study, but complete recordings, Canvas checkpoints, Gradescope, and staff feedback remain unavailable.
Lecture 1 uses an alien autoencoder, the scope of AI and ML, and AI history to establish the course's coordinate system: an intelligent system turns inputs into representations and decisions under uncertainty.
Lecture 2 decomposes search into a problem, frontier, and priority: UCS uses paid cost, Greedy uses estimated remaining cost, and A* combines them as `f=g+h`; tree and graph search require different optimality conditions.
Lecture 3 turns a single path into a contingent plan: minimax faces an optimal opponent, alpha-beta skips branches without changing the root value, and expectimax replaces worst-case choice with probability.
Lecture 4 exposes structure through variables, domains, and constraints, then upgrades DFS with backtracking, forward checking, AC-3, MRV, and LCV; the goal is to prove failure earlier.
Lecture 5 formulates machine learning through `X → Y`, loss, risk, and empirical risk minimization: a training set only gives average observed loss, while the real objective remains generalization over an unknown distribution.
Lecture 6 recursively grows a tree from decision stumps, measures label uncertainty with entropy, and selects splits by `I(Y;W)=H(Y)-H(Y|W)`; this is computationally practical greedy ERM, not a global optimal-tree guarantee.
Lecture 7 applies ERM to linear functions and squared loss, moves from a one-dimensional slope to `argmin ||y-Xθ||²`, and derives the normal equation when `XᵀX` is invertible.
Lecture 8 moves from a one-dimensional parabola to vector gradients and compares batch GD, SGD, and mini-batches; the learning rate determines whether updates converge, oscillate, or diverge.
Lecture 9 models P(y=1|x) with a sigmoid instead of directly predicting 0 or 1, learns parameters with cross-entropy and convex optimization, and extends naturally to softmax regression.
Lecture 10 uses φ(x) to let linear models express nonlinear functions, then controls the resulting overfitting with train/validation/test separation, L1/L2 regularization, and model selection.
Lecture 11 expands a logistic unit into a multilayer network: linear layers produce z, activations produce a, and multiple neurons jointly learn a feature transform trained through a final loss.
Lecture 12 treats a network as a computation graph: the forward pass stores intermediates, the backward pass propagates upstream gradients, and local linear, activation, and softmax rules compute every parameter gradient efficiently.
Lecture 13 separates alignment into specification, distribution shift, oversight, and corrigibility, then uses benchmark selection, leakage, and post-hoc selection experiments to show why a final paper cannot audit an autonomous research workflow.
Lecture 14 replaces dense image models with local connectivity and parameter sharing, moving from convolution, stride, padding, and pooling to AlexNet, GPU data parallelism, ResNet skip connections, and BatchNorm.
Lecture 15 splits a pretrained model into representation g and task head h: freeze g and train only the head, or fine-tune some or all parameters at a smaller learning rate depending on data volume and source-target distance.
Lecture 16 starts from likelihood p(D|θ), uses i.i.d. to factor the joint probability and logs to turn products into sums; Bernoulli MLE yields sample proportions, conditional Bernoulli yields logistic cross-entropy, and Gaussian noise yields squared error.
Lecture 17 first decides how text becomes tokens, then uses N-grams to turn sequence probability into conditional probabilities estimated from corpus counts. Tokenization is the first design decision about what a model can see.
Lecture 18 truncates the chain rule with an N-gram Markov assumption, estimates probabilities from corpus counts, and contrasts greedy, categorical, and temperature sampling. The real bottlenecks are zero probability for unseen contexts and a fixed window.
Lecture 19 builds a minimal next-token model from two embedding matrices, dot-product similarity, softmax, and cross-entropy. Shared vector parameters replace the isolated count cells of an N-gram table.
Lecture 20 expands one-token embeddings into sequences, adds positional information, derives Q/K/V scaled dot-product attention and causal masking, and assembles multi-head blocks into a GPT-2 skeleton.
Lecture 21 formulates stochastic sequential decisions as an MDP with known dynamics, defines value and Q-values through Bellman backups, and solves for an optimal policy with value or policy iteration.
Lecture 22 keeps the MDP structure but removes known transitions and rewards. TD learning updates value from one sample, and Q-learning uses an off-policy target to learn optimal action values directly.
Lecture 23 replaces a huge Q-table with Qθ(s,a): first derive a gradient update for linear features from squared TD error, then add replay data and a fixed target network to form DQN.
Spring 2026 Lecture 24 is MCTS, not Fall 2026 LLM post-training. It allocates simulations through selection, expansion, rollout, backup, and UCB, then connects policy/value heads and self-play to AlphaZero.
Lectures 1–12 form one decision pipeline: define states, moves, and objectives, then use heuristics, losses, regularization, and backpropagation to control an otherwise intractable search space.
Stage II uses HW8 and HW11 to test whether representation, computation graphs, training, transfer, and generation actually connect, rather than treating CNNs and Transformers as diagrams to memorize.
Stage III connects value, policy, bootstrapping, function approximation, and MCTS into AlphaZero: a network supplies priors and estimates, search improves decisions, and self-play creates the next training set.
CS124 Winter 2026 opens by mapping a ten-week path from tokenization and classification to retrieval, speech, networks, and LLMs, while PA0 establishes the Jupyter environment used throughout the quarter.
Lecture 1 of the 2026 course builds the vocabulary shared by the rest of the course: perceptrons, forward propagation, loss, and gradient descent.
Lecture 2 of the 2026 course addresses data where order changes meaning—text, audio, and time series—and connects directly to music generation in Lab 1.
Lecture 3 of the 2026 course moves from image tensors, convolution, and pooling to recognition systems, preparing for MNIST and face detection in Lab 2.
Lecture 4 of the 2026 course separates generative from discriminative tasks, organizes VAE, GAN, and diffusion objectives, and leads into Lab 2’s DB-VAE.
Lecture 5 of the 2026 course connects agent, environment, state, action, reward, and policy into an interaction loop, introducing credit assignment and exploration.
Lecture 6 of the 2026 course places deep learning in emerging applications and real constraints, emphasizing data, outputs, evaluation, and failure conditions.
Lecture 7 of the 2026 course starts from Asimov’s literary laws and examines modern safety protocols through traces, test data, and continuous evaluation.
Lecture 8 of the 2026 course uses the scientific-discovery loop to show how simulators, AI emulators, and experiments cooperate instead of reducing science to generic prediction.
Lecture 9 of the 2026 course starts with GPU memory pressure and moves through checkpointing, offloading, ZeRO, FSDP, and multiple forms of parallelism.
In the 2026 lab, students cover tensors, autograd, and modules before turning ABC notation into character sequences for LSTM music generation.
In the 2026 lab, part 1 classifies MNIST with dense and convolutional networks; Part 2 learns a facial latent distribution with a DB-VAE and changes training sampling.
In the 2026 lab, students build chat templates and generation with LFM2-1.2B, adapt style through LoRA, and combine OpenRouter with Opik for a judge workflow.
Spring 2026 CS185/285 publishes slides for 25 lectures, nine discussion units, five assignments, and starter code; current recordings require bCourses access, while HW4 defaults to an H100, so this is not a zero-cost open course.
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.
MIT 6.S191's 2026 edition publishes nine lecture videos, slides, three software labs, and solutions, making it an A3 self-study course. The supplied path still depends on Google/Colab, Comet, and OpenRouter for Lab 3, while unaffiliated learners do not receive MIT credit, project feedback, or API credits.
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.
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.
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.
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.
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.
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 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.
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.
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.
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 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.
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.
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.