So, lets imagine there is some problem called graph partitioning which is a real problem.

I'm partitioning a partitioned unit.

Let's go over now how we can actually solve the top K problem using this idea of partitioning.

This is called fuel partitioning in the lingo.

When to set off is a difficult problem.

The problem was where to set up the tent.

Spectral partitioning, L1 minimization. This comes from the optimization community.

A committee has been set up to investigate the problem.

So, problem is defined by the set of possible inputs.

Problem solving works when Problem solving technology works when the following set of conditions is true

So that's how can use the solution into the clique problem to solve the independent set problem.

Welcome to our first problem set covering visualization. Let's get started.

The government has set up a committee to look into the problem.

It's sort of cool that Top K via partitioning as just described works.

The time it takes to run Top K via partitioning on one element.

It doesn't solve the TopK problem better than the randomized algorithm we talked about the partitioning algorithm but it does it pretty fast and there's other uses for this.

It's kind of a partially sorted list, and we call this partitioning around V.

Consider a supervised learning classification problem where you have a training set like this.

So just like the last problem, I like to set y equal to this.

No problem, no problem, no problem, no problem...

No problem, no problem, no problem, no problem... Mom!

No problem, no problem, no problem...

And I decided that I was going to set out to try to solve this problem.

Now, the partitioning algorithm is going to split this list L into a left and a right.

Do we know that graph partitioning is in NP, and therefore an exponential time solution would exist?

Next problem, problem 40.

Next problem, problem 20.

Next problem, problem 25.

Next problem, problem 37.

So... almost done. This is the hard part. All right, there we go, set up the problem.

Back set! Front set!

Hmm? No problem. No problem.

One more problem, problem 52.

So this is a set, this includes a bunch of problems, a problem is in the set if there's an algorithm for that problem such as that when inputs size n, the running time for solving that problem is in big O then to the k for some constant k.

No problem, no problem, no problem, no problem... What did you do to him?

This process is an engineering problem, a mechanical problem, a logistical problem, an operational problem.

So when theta with the model selection problem like this is, instead of using the test set to select the model, we're instead going to use validation set or the cross validation set to select the model.

PROBLEM

Problem?

PROBLEM

Yeah, no problem. No problem. Okay.

There's wonderfully written books that layout problem after problem after problem.

Eventually, I set about writing my book, really to demonstrate the extent of this problem on a global scale.

Well, the so what? comes when start thinking about the same problem set up just a little bit differently.

The supervised learning problem in this case is given a set of labels to fit a hypothesis to it.