For this exercise, refer to Chapter 7 of Mitchel's Machine Learning. Consider a learning problem with a single real-valued feature X = R, and identical sets of hypotheses and concepts CH Hypotheses are a set of open intervals over the real numbers What is the probability that a hypothesis consistent with m examples of this target concept (that is the training error is 0) will have true error at least e? Solve this by computing the VC dimension of the hypothesis space For this exercise, refer to Chapter 7 of Mitchel's Machine Learning. Consider a learning problem with a single real-valued feature X = R, and identical sets of hypotheses and concepts CH Hypotheses are a set of open intervals over the real numbers What is the probability that a hypothesis consistent with m examples of this target concept (that is the training error is 0) will have true error at least e? Solve this by computing the VC dimension of the hypothesis space