Summary cont.

• Learning problem needs well-specified task, performance metric, and source of training experience.
• Machine Learning approach involves a number of design choices: type of training experience, target function, representation of target function, an algorithm for learning the target function from the training data.
• Learning involves searching the space of possible hypotheses. Different learning methods search different hypothesis spaces (numerical functions, neural networks, decision trees, symbolic rules).
• There are some theoretical results which characterize conditions under which these search methods converge toward an optimal hypothesis.