Flashcard Set: Beginning Artificial Intelligence
| 0 memorized | 99 to go | |
|
|
|
|
New Card Training
These work outs focus on cards you've not yet memorized successfully. Try it - works like a charm.
Review Sessions
These work outs focus on cards you have memorized successfully.
Print This Set
Browse individual flashcards :
AI: Gradient descent for multiple va... AI: feature scaling for gradient des... AI: mean normalization for gradient d... AI: fully versus partially observable AI: logical positivism AI: describe notion of intractability AI: theory that helps you to recogniz... AI: field that handles how to make ra... AI: task environment AI: simple reflex agent AI: open loop system AI: 8 queens problem AI: 2 ways to formulate 8 queens prob... AI: how to know if gradient descent i... AI: touring problem AI: what is traveling salesperson pro... AI: in search treee what is distincti... AI: 4 ways to evaluate an algorithm's... AI: 3 ways to assess AI search algo... AI: 2 types of search strategies base... AI: number of nodes in memory for bre... AI: what will cause uniform search fu... AI: strategy of uniform cost search AI: normal equation method to minimiz... AI: when minimizing cost function for... AI: reasons normal equation for solvi... AI: what is a vector AI: sigmoid function AI: logistic function AI: shape of logistic function AI: interpretation of hypothesis out... AI: prior probability (in context of... AI: the complete set of random varia... AI: probability distributions for con... AI: notations for posterior probability AI: what is the probability product rule AI: what is the probability of a or b AI: with full joint distribution of ... AI: in full joint distribution what i... AI: what is conditional independence AI: conditional independence of 2 var... AI: conceptually, Z _______ X and Y ... AI: common pattern in which a single ... AI: naive Bayes model AI: before evidence is obtained, we t... AI: after evidence is obtained we tal... AI: utility theory says ______ AI: decision theory AI: fundamental idea of decision theory AI: each random variable has a ____ ...
AI: Gradient descent for multiple va... AI: feature scaling for gradient des... AI: mean normalization for gradient d... AI: fully versus partially observable AI: logical positivism AI: describe notion of intractability AI: theory that helps you to recogniz... AI: field that handles how to make ra... AI: task environment AI: simple reflex agent AI: open loop system AI: 8 queens problem AI: 2 ways to formulate 8 queens prob... AI: how to know if gradient descent i... AI: touring problem AI: what is traveling salesperson pro... AI: in search treee what is distincti... AI: 4 ways to evaluate an algorithm's... AI: 3 ways to assess AI search algo... AI: 2 types of search strategies base... AI: number of nodes in memory for bre... AI: what will cause uniform search fu... AI: strategy of uniform cost search AI: normal equation method to minimiz... AI: when minimizing cost function for... AI: reasons normal equation for solvi... AI: what is a vector AI: sigmoid function AI: logistic function AI: shape of logistic function AI: interpretation of hypothesis out... AI: prior probability (in context of... AI: the complete set of random varia... AI: probability distributions for con... AI: notations for posterior probability AI: what is the probability product rule AI: what is the probability of a or b AI: with full joint distribution of ... AI: in full joint distribution what i... AI: what is conditional independence AI: conditional independence of 2 var... AI: conceptually, Z _______ X and Y ... AI: common pattern in which a single ... AI: naive Bayes model AI: before evidence is obtained, we t... AI: after evidence is obtained we tal... AI: utility theory says ______ AI: decision theory AI: fundamental idea of decision theory AI: each random variable has a ____ ...
