AdaBoost assumption of weak classifier I've read and think I got a good grasp of the math behind AdaBoost, but I wasn't able to understand why AdaBoost requires a weak base-classifier?
Specifically, I'm dealing with AdaBoost using decision-tree as base-classifier. So, what if my DT wasn't "weak"? So instead of using a stump (DT with depth of 1), what should happen with AdaBoost if I used deeper tree for example? 
I found this, but I'm not it fully answers my question
 A: Practically, I'm unsure why one would need to rely on AdaBoost if we already had a strong classifier. Tl;dr: I don't believe that having a weak learner is requirement for AdaBoost to work.
I can try to walk through some of the analysis. We'll deal with empirical error, and then generalization error.
Empirical Error (train)
We define a weak learner as any classifier with error rate $\frac{1}{2} - \gamma$ for $\gamma \in (0, \frac{1}{2})$. In the case you're worried about, $\gamma$ is close to $\frac{1}{2}$.
Let $J(\theta^{(i)})$ be the error of AdaBoost after $i$ rounds; we can prove the following rate-of-convergence result:
$$J(\theta^{(i+1)}) \leq \sqrt{1 - 4\gamma^2} J(\theta^{(i)})$$.
This is proven by Duchi here. So if we denote $\gamma$ for the weak learner after round $i$ as $\gamma_i$, we can write
$$J(\theta^{(T)}) \leq \prod_{t=1}^T \sqrt{1 - 4\gamma_t^2} \leq exp\left(-2 \sum_{t=1}^T \gamma_t^2\right) \leq exp\left(-2\gamma^2T\right)$$
for $\gamma = \underset{t}{\min}\gamma_t$. So, basically, the empirical error vanishes exponentially. Note that this occurs regardless of $\gamma$; it simply trades off with $T$, so we don't necessarily need a particular type of weak learner, just any classifier with better-than-random error rate.
Generalization Error (test)
Dealing with generalization error is a little bit past my mathematical abilities. But, using basic statistical learning theory, it's a result from Vapnik (1971) that 
$$\varepsilon(h) \leq \hat{\varepsilon}(h) + O\left(\frac{1}{\sqrt{m}}\sqrt{d\log\frac{m}{d} + \log\frac{1}{\delta}}\right)$$ 
for classifiers under the empirical risk minimization (ERM) learning framework with high probability ($1-\delta$). Note $d = VC(\mathcal{H})$, the VC-dimension of the hypothesis class of $H$. We can treat AdaBoost this way since it is a classifier that tries to minimize some empirical risk (i.e. training loss) on a training dataset (this is hand-wavey, but the definition of ERM isn't the point here). I believe the notes here build on these principles and show a similar bound on the generalization error of Adaboost, that is;
$$\varepsilon(h) \leq \hat{\varepsilon}(h) + O\left(\frac{1}{\sqrt{m}}\sqrt{\frac{\log m\log|\mathcal{H}|}{\theta^2} + \log\frac{1}{\delta}}\right)$$ 
where they treat Adaboost as a max-margin classifier with margin $\theta$; unfortunately, I don't think I'll be much help in elucidating this particular formula. Note, however, that there is no dependence on $\gamma$ here except in the first $\hat{\varepsilon}(h)$ term, which vanishes regardless of $\gamma$ as $T \to \infty$. So a particular type of weak learner is again unnecessary -- we just need a better-than-random learner. 
