CS 221 - Artificial Intelligence

My twin brother Afshine and I created this set of illustrated Artificial Intelligence cheatsheets covering the content of the CS 221 class, which I TA-ed in Spring 2019 at Stanford. They can (hopefully!) be useful to all future students of this course as well as to anyone else interested in Artificial Intelligence.

Cheatsheets

Reflex
Reflex
  • Feature vector, score, margin, residual
  • Linear predictors, non-linear predictors
  • Loss minimization, stochastic gradient descent, backpropagation, regularization
PDF Web
States
States
  • Tree search, graph search, A star search
  • Learning costs, relaxation
  • Markov decision processes, game playing
PDF Web
Variables
Variables
  • Constraint satisfaction problem, backtracking search
  • Beam search, Gibbs sampling
  • Bayesian networks, probabilistic program, inference
PDF Web
Logic
Logic
  • Notations, knowledge base, forward inference, rule properties
  • Propositional logic, modus ponens, resolution rule, conjunctive normal form
  • First-order logic, substitution, unificaion
PDF Web

Would you like to see this set of cheatsheets in your native language? You can help us translating them on GitHub!