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.

And in the next video

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

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

Unknown Optimization

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.

Bugfixes and optimizations

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.

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.

We have to be correct.

Let's see.

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?

A cross platform tool for optimizing PNG and JPG files.

Optimum Power Calibration failed.

Compact Now

Optimize for large files

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

All right.

I gave you the problem of how to solve for known correspondence, which becomes a continuance optimization problem, but there is also a discrete problem here, which is does this lane marker correspond with this guy over here or to that guy over here?

But often domain specific languages do a better job at it.

exception 0 divided by 0 raises an exception.

I translated all sorts of speakers' talks.

Because of this it is problematic in a system of total dependency.

Let's get started with some problems.

I save it every time we're in this loop.

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

Remember how are we using optimization okay?

We have to get the same answer, just in less time.

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

This is the critical rule of optimization.

Its computational time is increasing