Some Issues in Machine Learning

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