Narrator Before diving into too much detail,

Chief Agent Yuri Marklov.

I'm Chief Agent Yuri Marklov of the F.S.B.

In fact, I didn't even tell you what the initial state was.

In normal life, there is a cost associated with moving.

Thrun So let's begin by taking the hidden out of the Markov model and study Markov chains.

We never brought planning and uncertainty together, uncertainty and learning, or planning and learning.

So most of this class focuses on Markov decision processes as opposed to partially observable Markov decision processes.

Now, before we get into the math of reinforcement learning,

In a state transition graph, like this, is a finite state machine, and it becomes Markov if the outcomes of actions are somewhat random.

Obviously the top state is much more attractive than the bottom state with minus 100.

It's applicable to many estimation problems, and it will be important going forward in this class.

You learned about localization.

So, we've learned quite a bit so far.

like A , depth first, right first and so on.

This is a great moment where I can test whether you really think like an artificial intelligence person.

Thrun

In this final question, I'd like you to learn the position parameters of Markov chains.

Again, what do you think could help us get the right answer this time?

So the answer here is the stationary distribution will have A occurring with 2 3 chance and B with 1 3 chance.

In this case, the problem really comes down to the naive Bayes assumption is a weak one, and the Markov assumption would do much better.

I'll briefly talk about POMDPs but not in any depth.

It's kind of the big thing in application and machine learning.

So what is a Markov decision process?

So we want the policy that maximizes that result.

Check that box if you think that would be helpful.

And that resolves simply to 0.4 if you work it all out.

This software carries out Markov Chain Monte Carlo calculations by the use of Gibbs Sampling.

That's like the fancy math notation, but what it really means is we just wait a long, long time.

This very simple model of just looking at unigram possibilities and looking at the edits achieves accuracy in the 80 range.

That's called a value.

leads to state S3.

What if you don't even know P the transition model of the world?

In summary, then, we've learned how to do a lot with MDPs especially using reinforcement learning.

Whereas if you need memory, it's partially observable.

It is Laplacian smoothing, which can be applied just exactly the same way we saw it in class in various contexts.

This is our sample for the initial state and all these transitinos over here are samples for the state transitions in this Markov chain.

And so the general, long term trends of evolution are roughly these five ubiquity, diversity, specialization, complexity and socialization.

Replication lag is the example of the loss of which property

Chronic viral ulcerative lesion.

So we throw away our old pi, pi1, and replace it with a new pi, pi2 which is a result of solving the MDP described by our new estimates of the utiliities.

including a man!

I talked to African American women, Asian American women,

AClD this stands for

My own definition of feminist is a feminist is a man or a woman who says (Laughter) (Applause) a feminist is a man or a woman who says