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.
Lecture 1 of Stanford CS221 Autumn 2025 develops operational representations and algorithmic intuition through Overview: Defining Intelligence Under Resource Constraints.
Lecture 2 of Stanford CS221 Autumn 2025 develops operational representations and algorithmic intuition through Learning I: From Computation Graphs to Linear Regression.
Lecture 3 of Stanford CS221 Autumn 2025 develops operational representations and algorithmic intuition through Learning II: Linear Classification, Features, and Cross-Entropy.
Lecture 4 of Stanford CS221 Autumn 2025 develops operational representations and algorithmic intuition through Learning III: Deep Networks as Composable Computation Graphs.
Lecture 5 models search with states, actions, successors, and costs, then uses acyclic dynamic programming to show that an efficient algorithm still solves the wrong problem when state omits information needed by the future.
Lecture 6 of Stanford CS221 Autumn 2025 follows the official material on Search II: Priorities in UCS and A* and makes its assumptions and limits explicit.
Lecture 7 of Stanford CS221 Autumn 2025 follows the official material on MDPs I: Putting Uncertainty into State Transitions and makes its assumptions and limits explicit.
Lecture 8 of Stanford CS221 Autumn 2025 follows the official material on MDPs II: Learning Q-Values Without a Transition Model and makes its assumptions and limits explicit.
Lecture 9 moves from tabular RL to function approximation, derives REINFORCE with the log-derivative identity, and connects the derivation to the executable PyTorch implementation.
Lecture 10 extends single-agent search into adversarial game trees: expectimax averages chance outcomes, minimax takes the opponent's worst case, and alpha-beta removes irrelevant branches without changing the answer.
Lecture 11 first learns game values from experience with temporal-difference updates, then moves from sequential play to simultaneous games described by mixed strategies, minimax guarantees, and Nash equilibria.
Lecture 12 builds a joint distribution from random variables and factors, then uses Bayesian-network factorization to express conditional independence and make conditioning and marginalization executable.
Lecture 13 replaces costly exact inference with Gibbs sampling: resample one variable at a time from a conditional determined by its Markov blanket, then approximate query probabilities with sample frequencies.
Lecture 14 moves from maximum-likelihood counts and Laplace smoothing with complete data to EM, which alternates posterior responsibilities for latent variables with parameter updates.
Lecture 15 separates propositional syntax from semantics: model checking defines entailment through satisfying assignments, SAT finds witnesses, and inference rules must be judged for both soundness and completeness.
Lecture 16 compresses knowledge across objects with predicates, quantifiers, and functions, then derives conclusions through substitution, unification, and definite-clause forward inference while exposing termination and completeness limits.
Lecture 17 defines a language model as a chain-rule factorization of sequence probability, compares n-gram and neural conditional models, and shows how sampling, temperature, and evaluation shape generation.
Lecture 18 classifies AI's social effects as benefits, misuse, accidents, and structural harms, then connects fairness audits, research ethics, copyright, and platform terms to accountable institutional choices.
Lecture 19 uses the Economics of AI deck to connect compute, data, distribution, and organizational complements to GDP, labor, and ideas-driven growth.
Lecture 20 is Percy Liang's fireside chat on career and research, CS221 and Stanford, and AI's future, with every attribution tied to the official video and editorial synthesis kept separate from auto-caption uncertainty.