A concept class $C$ is PAC-learnable if there exists an algorithm that can output a hypothesis with probability at least $(1-\delta)$ (the "probably" part), and an error that is less than $\epsilon$ (the "approximately" part), in time that is polynomial in $1/\epsilon$, $1/\delta$, $n$ and $|C|$.
Tom Mitchell defines an upper bound for the sample complexity, $m >= 1/\epsilon (ln(|H|) + ln(1/\delta))$ for the finite hypotheses. Based on this bound, he classifies whether target concepts are PAC-learnable or not. For example, $n$ conjunction boolean literal concept class.
It seems to me that PAC-learnability seeks to act more like a classification of certain concept classes.
Are there any practical purposes for knowing whether a concept class is PAC-learnable?