You have a set of examples,

and this set of examples actually say,

in this situation, you should do that.

Okay. So, very simply put,

that's what we call inductive learning is,

we have a set of examples,

which is a function from

states to outcomes,

which is the gold truth,

the gold standard which we want to learn.

And so, what we want to do is,

we want to find a hypothesis,

a function that behaves similarly,

or ideally exactly like F,

on the examples we have already seen,

and that has a good prediction quality

for future unseen examples.

Kind of elephant in the room here is that,

the hypothesis has to come from

some kind of a hypothesis space.

And this hypothesis space

is something that kind of in

the background determines a lot of things.

We've looked at these examples of curve fitting,

where we've looked at different hypothesis spaces,

linear polynomials,

quadratic polynomials,

order four polynomials,

order gazillion polynomials,

or something like that.

And you can see that what

the best hypotheses are

depends on the hypothesis space.

Okay. Sometimes, we have consistent,

we have consistent hypotheses,

which means they are okay on all of the examples.

Sometimes, we have non-consistent or

partially consistent hypotheses,

all depending on what you're allowed

to pick the hypothesis space. Yes.

Do you assume that the training set

does not contain all values?

Or does it really matter because

you're trying to learn this training set

and it contains all values,

you might know something wrong.

Yes. Right. If we have wrong examples,

we have to deal with that.

Okay. So, we might really say,

we need to have outlier detect,