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What algorithms can approximate functions well (and when)?
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How does number of training examples influence accuracy?
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How does complexity of hypothesis representation impact it?
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How does noisy data influence accuracy?
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What are the theoretical limits of learnability?
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How can prior knowledge of learner help?
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What clues can we get from biological learning systems?
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How can systems alter their own representations?
