Quote:
Originally Posted by Rahul Sinha
Let us try and decode the experiment: X is not the whole input space but just a fraction of it. card(X) = 10000 and not infinity. Usually, we will neither have the time nor the energy to flip a coin infinite number of times.

I don't understand why do you suppose that whole input space should be infinite and neither do I understand how this coin tossing relates to learning
In particular what is
and what is the target function.
Quote:
Yes, you did memorize the mu but if N is large, it can not be too bad! That's the key

I beleive it's not true. For example no matter how much data you give me I can always come up with a polynomial of degree equal to the number of training examples which is perfect in sample. Suppose you gave me 20 points
from linear function and I generate a polynomial of degree 20. I think it will be really bad out of sample.
Also, I found a question similar to mine and posted a reply
here.