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?)
Quote:

All times are GMT 7. The time now is 09:18 AM. 
Powered by vBulletin® Version 3.8.3
Copyright ©2000  2019, Jelsoft Enterprises Ltd.
The contents of this forum are to be used ONLY by readers of the Learning From Data book by Yaser S. AbuMostafa, Malik MagdonIsmail, and HsuanTien Lin, and participants in the Learning From Data MOOC by Yaser S. AbuMostafa. No part of these contents is to be communicated or made accessible to ANY other person or entity.