Here's the idea for gradient descent.

We study again our die. You observe the following sequence 1, 2, 3, 2.

I will now teach you about estimators.

My first guess was wide off the mark.

This is a definition that Wikipedia gives you.

What this says is based on the outcome, we believe for the parameter µ.

It's hours, people per hour.

And our program set to cut to the nominal part diameter, these jaws are ready to cut As recommended in the first video

To do so, I assume that half of the people are of type A and half of the people are of type B.

There's many different ways to implement this.

I remember the first time we instigated the protocol.

Whereas those guys over here likely have died out.

And if we look at the code, that's exactly what happens.

The initial probability for rain is 0 because we are just encountering 1 initial day and it's sunny.

However I'd like to do this with Laplacian smoothing with k 1.

Press the TOOL OFFSET MEASURE key

Narrator And the answer is there's 8 different messages of which 3 are spam.

And very certainly, the answer is yes.

My first guess was wide off the mark.

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Supposed suicide.

In both of the transitions we go from rain to sun. There is no instance of rain to rain.

One technique to deal with the overfitting problem is called Laplace smoothing.

You might remember all this work with Laplace smoothing, but for now we keep it simple, just maximum likelihood.

These are, in total, 7 different days, and we wish to estimate all those probabilities over here, including the initial distribution for the first day using maximum likelihood.

There is 5 data points.

Here is our sequence. There's a couple of sunny days 5 in total a rainy day, 3 sunny days, 2 rainy days.

We find for day 0 we had rain, and maximum likelihood would just say the probability for day 0 is 1.

Please use Laplacian smoothing for this with K equals 1.

I just want to tell you what you've learned, because you did amazingly well.

In fact, we're going to use a very simple rule.

W 0, where 0 is your iteration number, and then you up with it iteratively.

We're given that the list is positive integers.

So, here's another quiz.

In 3 cases, A is followed by A over here, over here, and over here.

And in the second box, give me an estimator that has one fake data point.

If we look at the numbers over here, we get 0.5833 and 0.5986.

So the algorithm looks as follows.

So the way that we would refer to this is the probability of both of these happening

Let me ask you again. Suppose you make 111 coin flips.

Stop estimating backup size

Simple extrapolation.

Consider the following world with 4 states A, B, C, and D.