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

VIP cheatsheets for Stanford's CS 221 Artificial Intelligence observed · 2026-08-28

github.com/afshinea/stanford-cs-221-artificial-intelligence · homepage · MIT (permissive) observed · 2026-08-28

Health v2 · maintenance only

32/100

  • Activity 0
  • Release rhythm 35
  • Longevity 100

Flags: no_releases

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.

  • gap_med: n/a
  • age_days: 2658
  • days_rel: n/a
  • days_push: 2451
  • n_releases_24m: 0

Full methodology

Adoption not part of the score

2981 stars · 567 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

learning-resource · maturity stable

documentation artificial-intelligence education tutorials cross-platform cheatsheets stanford-cs221 study-notes a-star markov-decision-processes constraint-satisfaction bayesian-networks pdf

2 sources

Member repositories

For agents

markdown · JSON · MCP: product_card(name="afshinea/stanford-cs-221-artificial-intelligence")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem