7: Past paper stuff Flashcards
Why is the A* search algorithm the following:
a) optimal?
b) complete?
c) optimally efficient?
d) focused?
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What is the minimax search algorithm? Which parts of it are informed and uninformed?
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What is backprop? What does it do?
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What are hyperplanes?
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How do you calculate weight changes?
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How do you calculate weight changes for hidden units in neural networks?
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What are convolutional neural networks?
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What is Bayes’ rule?
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What is a causal probability?
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What is a diagnostic probability?
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How do you convert from a causal to a diagnostic probability uisng Bayes’ rule?
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What are FOPL semantics?
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What is entailment?
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What is a perfect information game?
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What is a 2-player perfect information game?
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What is an endgame database?
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How do you construct an endgame database?
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How can you use statements to prove that certain entailments are true?
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What are complete and incomplete search methods?
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What is the WalkSAT search algorithm?
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How does the WalkSAT search algorithm work?
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What is a probability space?
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What are joint probability distributions?
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How do you deduce probabilities from joint probability distributions? What method does it use?
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How do you find the number of node iterations needed for each search algorithm to solve a certain problem?
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What is Manhattan distance?
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What is NYC cab distance?
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How do you manually write out A* search algorithm trees for a certain problem?
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How do you manually write out greedy search algorithm trees for a certain problem?
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How do you manually write out Uniform-cost search algorithm trees for a certain problem?
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How do you manually write out depth-first algorithm trees for a certain problem?
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What does it mean for a heuristic to be admissible? How does this apply to the A* search algorithm?
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What are binary game search trees?
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What is the alpha-beta search algorithm?
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