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.

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

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