# afshinea/stanford-cs-221-artificial-intelligence

VIP cheatsheets for Stanford's CS 221 Artificial Intelligence

Repository: https://github.com/afshinea/stanford-cs-221-artificial-intelligence
Canonical: https://ross.abutalabs.com/products/stanford-cs-221-artificial-intelligence
Homepage: https://stanford.edu/~shervine/teaching/cs-221/
License: MIT
License Family: permissive
Topics: cheatsheet, artificial-intelligence, markov-decision-processes, a-star, constraint-satisfaction-problem, bayesian-networks, data-science
Last push: 2019-12-17T05:18:38+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2658, "days_push": 2451, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2981, forks 567 (observed 2026-08-28T04:07:33.166473+00:00)

## What it is
A collection of illustrated PDF cheatsheets summarizing Stanford's CS 221 Artificial Intelligence course, covering reflex-based, states-based, variables-based, and logic-based models. The material is available in multiple languages and on a companion website.

## Use cases
- study for Stanford CS 221 artificial intelligence course
- review A* search and tree search concepts before an exam
- get a quick refresher on Markov decision processes
- understand constraint satisfaction problems and backtracking
- learn Bayesian networks and inference basics
- find a concise summary of propositional and first-order logic
- prepare for an AI course in a language other than English

## When to choose
- you want condensed, illustrated summaries of core AI course topics
- you are a CS 221 student or self-learner needing quick reference material
- you prefer cheatsheets in English, French, Turkish, or other translated languages

## When to avoid
- you need hands-on code examples or implementations
- you want a comprehensive AI textbook or in-depth tutorials
- you need up-to-date content on modern deep learning or LLM topics

## Facets
- artifact type: learning-resource
- maturity: stable
- function: documentation
- domain: artificial-intelligence, education, tutorials
- platform: cross-platform
- tags: cheatsheets, stanford-cs221, study-notes, a-star, markov-decision-processes, constraint-satisfaction, bayesian-networks, pdf

## Member repositories
- afshinea/stanford-cs-221-artificial-intelligence (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:33.166473+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:31:29.371255+00:00, confidence not recorded.
  - readme: https://github.com/afshinea/stanford-cs-221-artificial-intelligence (fetched 2026-08-28T04:07:33.166473+00:00, sha a7860de6c678)
  - homepage: https://stanford.edu/~shervine/teaching/cs-221/ (fetched 2026-08-29T09:46:51.759397+00:00, sha 129fe3144459)
- Data as of 2026-08-30T08:39:29.467469+00:00.