Doubt from lecture 2(Is learning feasible?)
At 31 minutes mark professor has assumed that the input samples come from a probability distribution. My question is why do we make this assumption? Because throughout the lecture we haven't make use of this assumption anywhere.

Re: Doubt from lecture 2(Is learning feasible?)
The assumption made it possible to invoke Hoeffding inequality. Without a probability distribution, one cannot talk about the probability of an event (the lefthandside of the inequality). The specifics of the probability distribution don't matter here, any distribution will do.

Re: Doubt from lecture 2(Is learning feasible?)
Can't I start talking about hypothesis analogy without making this assumption?
I mean if i say that a hypothesis is analogous to a bin and then I say that for any hypothesis there is a probability that that it will make a wrong classification in the bin and in the sample with probability \mu & \vu. And then go ahead with hooeffding's inequality. In doing so do I really need that assumption? 
Re: Doubt from lecture 2(Is learning feasible?)
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