Sandbox::https://github.com/stanford-cs221/lectures

CS221 Autumn 2026 Lectures

Welcome to CS221! This repository contains all the (executable) lectures, which are made using edtrace.

Each lecture consists of stepping through Python files which you can either view in your browser on the links below or run locally if you clone this repository. If you notice any problems or have any questions, please file an GitHub issue or submit a pull request.

To run locally:

apt-get install graphviz
uv sync
rc
python -m edtrace.execute -m welcome

git clone git@github.com:percyliang/edtrace
(cd edtrace/frontend && npm i)
npm --prefix edtrace/frontend run dev -- --port 2210

Lecture 1 (Tue Sep 22): Overview

Lecture 2 (Thu Sep 24): Learning I

Lecture 9 (Tue Oct 20): RL III

Lecture 10 (Thu Oct 22): RL IV

improved_policy_gradient

Lecture 11 (Tue Oct 27): Games I

games

Lecture 12 (Thu Oct 29): Games II

Lecture 13 (Thu Nov 5): Bayesian Networks I

bayes

Lecture 14 (Tue Nov 10): Bayesian Networks II

gibbs_sampling

Lecture 15 (Thu Nov 12): Bayesian Networks III

bayes_learning

Lecture 16 (Tue Nov 17): Logic I

propositional_logic

Lecture 17 (Thu Nov 19): Logic II

first_order_logic

Lecture 18 (Tue Dec 1): AI & Society

society

Name		Name	Last commit message	Last commit date
assets		assets
images		images
var		var
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
backpropagation.py		backpropagation.py
bayes.py		bayes.py
bayes_learning.py		bayes_learning.py
compile.sh		compile.sh
deep_learning.py		deep_learning.py
first_order_logic.py		first_order_logic.py
function_approximation.py		function_approximation.py
games.py		games.py
gibbs_sampling.py		gibbs_sampling.py
history.py		history.py
improved_policy_gradient.py		improved_policy_gradient.py
index.html		index.html
linear_classification.py		linear_classification.py
linear_regression.py		linear_regression.py
mdp.py		mdp.py
policy_gradient.py		policy_gradient.py
propositional_logic.py		propositional_logic.py
pyproject.toml		pyproject.toml
rc		rc
reinforcement_learning.py		reinforcement_learning.py
search.py		search.py
simultaneous_games.py		simultaneous_games.py
society.py		society.py
start-server.sh		start-server.sh
td_learning.py		td_learning.py
tensors.py		tensors.py
ucs_astar.py		ucs_astar.py
util.py		util.py
uv.lock		uv.lock
welcome.py		welcome.py

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CS221 Autumn 2026 Lectures

Lecture 1 (Tue Sep 22): Overview

Lecture 2 (Thu Sep 24): Learning I

Lecture 3 (Tue Sep 29): Learning II

Lecture 4 (Thu Oct 1): Learning III

Lecture 5 (Tue Oct 6): Search I

Lecture 6 (Thu Oct 8): Search II

Lecture 7 (Tue Oct 13): RL I

Lecture 8 (Thu Oct 15): RL II

Lecture 9 (Tue Oct 20): RL III

Lecture 10 (Thu Oct 22): RL IV

Lecture 11 (Tue Oct 27): Games I

Lecture 12 (Thu Oct 29): Games II

Lecture 13 (Thu Nov 5): Bayesian Networks I

Lecture 14 (Tue Nov 10): Bayesian Networks II

Lecture 15 (Thu Nov 12): Bayesian Networks III

Lecture 16 (Tue Nov 17): Logic I

Lecture 17 (Thu Nov 19): Logic II

Lecture 18 (Tue Dec 1): AI & Society

Lecture 19 (Thu Dec 3): Fireside Chat, Conclusion

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CS221 Autumn 2026 Lectures

Lecture 1 (Tue Sep 22): Overview

Lecture 2 (Thu Sep 24): Learning I

Lecture 3 (Tue Sep 29): Learning II

Lecture 4 (Thu Oct 1): Learning III

Lecture 5 (Tue Oct 6): Search I

Lecture 6 (Thu Oct 8): Search II

Lecture 7 (Tue Oct 13): RL I

Lecture 8 (Thu Oct 15): RL II

Lecture 9 (Tue Oct 20): RL III

Lecture 10 (Thu Oct 22): RL IV

Lecture 11 (Tue Oct 27): Games I

Lecture 12 (Thu Oct 29): Games II

Lecture 13 (Thu Nov 5): Bayesian Networks I

Lecture 14 (Tue Nov 10): Bayesian Networks II

Lecture 15 (Thu Nov 12): Bayesian Networks III

Lecture 16 (Tue Nov 17): Logic I

Lecture 17 (Thu Nov 19): Logic II

Lecture 18 (Tue Dec 1): AI & Society

Lecture 19 (Thu Dec 3): Fireside Chat, Conclusion

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