- What algorithms exist for learning general target from specific training example ?
- In what settings will particular algorithms converge to the desired function, given sufficient training data ?
- Which algorithms perform best for which types of problems and representations ?
- How much training data is sufficient ?
- What general bounds can be found to relate the confidence in learned hypothesis to the amount of training experience and the character of the learner’s hypothesis space ?
- When and how can prior knowledge held by the learner guide the process of generalizing from example ?
- Can prior knowledge be helpful even when it is only approximately correct ?
- What is the best strategy for choosing a useful next training experience, and how does the choice of this strategy alter the complexity of the learning problem ?
- What is the best way to reduce the learning task to one or more function approximation problem ?
- What specific functions should the system attempt to learn ? Can this process itself be automated ?
- How can the learner automatically alter its representation to improve its ability to represent and learn the target function ?
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