CS 221 ― Artificial Intelligence
My twin brother Afshine
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.
Would you like to see this set of cheatsheets in your native language? You can help us translating them on GitHub!
- • 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