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Flashcards

12 Learning

Timo Bierwirth Artificial Intelligence German 9 cards

Cards in this deck (9)

  1. 1What is the definition of learning?Improving performance on future tasks after making observations about the world
  2. 2Which types of learning are there?-Unsupervised learning: in input
    -Reinforcement learning: from series of rewards or punishments
    -Supervised learning (!): observing example input-output pairs & function that maps from input to output
  3. 3What is crucial for supervised learning?Hypothesis selection: maximum probability that a hypothesis belongs to a data set - check whether hypothesis generalizes wll & predicts other values outside the training set
  4. 4Which strategy does a decision-tree-learning algorithm adopt?Greedy divide-and-conquer:
    -Test most important attribute first (splits examples into subsets that are ideally "all positive" or "all negative")
    -Test divides problem into two smaller subproblems that can be resolved recursively
  5. 5What can you do when remaining examples are all positive or all negative?Return yes or no and done
  6. 6What can you do when some examples are positive and some are negative?Choose best attribute to split them
  7. 7What can you do when there are no examples left?Return default value from plurality classification of all examples that were used in constructing the node´s parent (most common output value)
  8. 8What can you do when there are no attributes left, but positive and negative examples?There is a conflict because of noise in the data and domain is nondeterministic -> return plurality classification
  9. 9How can the importance of an attribute be quantified?Entropy: measure of the uncertainty of a random variable - acquisition of information corresponds to a reduction in entropy

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Artificial Intelligence Flashcards on 12 Learning, created by Timo Bierwirth on 25/02/2018.
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Timo Bierwirth
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