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1

The following answer is based on chapter 6/7 of the book ┬╗Understanding Machine Learning: From Theory to Algorithms┬ź, by Shalev-Shwartz and Ben-David (especially Example 7.1). It states that the class $\mathcal{H}$ of all polynomial classifiers over $\mathbb{R}$ is not PAC learnable ($\mathrm{VCdim}(\mathcal{H}) = \infty$). We might rewrite $\mathcal{H}$ as ...


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Before we try to get into ergodic or whatever else, let's try to understand what phenomenon a mathematician or scientist is trying to (or could be trying to) model with AEP. Well Asymptotic for very large $n$, a lot of coin flips, after a long time, etc ... Equipartition Equally distributed amongst some boxes or bins, Uniformly random, Equilibrium ...


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