And in the next video

We maximized the posterior probability of data logarithmic is a monotonic function, and I put a minus sign over here so the optimization problem becomes a minimization problem.

We know that we are optimized as human beings, to do hard and meaningful work.

I know that you might be wondering how is it that the optimization problem I wrote down in the previous slide, how does that lead to this large margin classifier.

Best

Optimized

Compaction

Optimize

Optimization

So let's try to understand the optimization problem in the context of, what would it take to make this first term in the objective equal to zero, because you know, maybe we'll set C to some huge constant, and this will hope, this should give us additional intuition about what sort of hypotheses a support vector machine learns.

So each state, except for the absorbing states, we have to define an action to define a policy.

Always Best

Compact Folder

Compacting database

Is that supervised, unsupervised or reinforcement learning?

Optimize for large files

the only problem was how to slow the aging process once the subject had reached the desired physical age.

Now this is the point in the Talk where I'm supposed to come up with some kind of a solution for the patent system.

Unknown Optimization

So it's a constraint problem. And there's a constraint that must be satisfied.

I care about the number that contain my event right here.

Over the entire course, we've seen lexing, parsing we just saw optimizing.

Dynamic programming is the only one that's universal, and smoothing is the only local operation whereas those over here all optimize a global planning problem.

There was one missing step in this video which is, why is it that the optimization problem we wrote down on these slides, how does that actually

Bugfixes and optimizations

Second, understanding society and culture.

Here is what I mean, to give you a concrete example, suppose I had a minimization problem that you know minimize over a real number u of u minus 5 squared, plus 1, right.

Well, that's it for now.

So there's plenty of room for creativity in optimization.

It's made into a grade where it's not usable for a nuclear weapon, but they love this stuff.

Performing Optimum Power Calibration

AutoOpt Molecule

Compact the current database project

So the planning problem is now the problem which relies on an action to each possible state.

Maximum time allowed to answer.

And that's the difference between problems that are additive,

But I'd like to give you a feeling for why this is important, and what type of problems you can solve with this, that you could never possibly solve with MDPs.

We have to be correct.

Let's see.

But does it work? Which of the following will our optimizer do? So let's see how well you really understand the optimizer we just wrote.

Removing the minimum value of a heap is log n.

And I cut and pasted part of it but they want us to determine what was the first incorrect step that they did in this problem?

What's wrong with the inertial dampeners?

The worst trouble imaginable.

Restricted Overwrite