So, that's anomaly detection.

So, what is anomaly detection?

Is this an anomaly or not?

X1 test is not an anomaly.

Another example of anomaly detection is manufacturing.

So, is that an anomaly or not?

So, the anomaly detection problem is the following.

For direct sales though, this is the anomaly.

Here are some examples of applications of anomaly detection.

So putting everything together, here is our anomaly detection algorithm.

So, what, she wanted to know, could explain this anomaly?

It was an anomaly, and she kept puzzling over anomalies.

I'd like to tell you about a problem called Anomaly Detection.

Can I come and interview you about your special brain anomaly?

In this video lets apply that to develop an anomaly detection algorithm.

They thought this was an anomaly, but it's happened three months straight.

More formally in the anomaly detection problem, we're give some data sets, x1 through

Palestine is something of a cartographic anomaly because it's two parts Palestinian, one part Israel.

If x tests were all the way out there, then we would call that an anomaly.

I was an anomaly, and my self was rooting around for definition and trying to plug in.

And in the video after this, we'll start applying the Gaussian distribution to developing an anomaly detection algorithm.

In the next video, we'll start to take this and apply it to develop the anomaly detection algorithm.

It's an anomaly, it's a place where the system actually is at odds with who we are as people.

And this is a little interesting anomaly that you get a little bit more interest for one month than three months.

I believe you may have a very special brain anomaly that makes you ... special, and interested in the predatory spirit, and fearless.

And in 1976, that was sort of an anomaly, to be a 10 year old kid that had access to a camera.

The way we are going to address anomaly detection, is we are going to model p of x from the data sets.

And what she did was show children that yellow ones made it go and red ones didn't, and then she showed them an anomaly.

In this video we talked about how to estimate p of x, the probability of x, for the purpose of developing an anomaly detection algorithm.

Points that are even further out have somewhat lower probability, and the point that's way out here, the point that's way out there, would be an anomaly.

So this is less than epsilon and so we'll say that that is indeed an anomaly, because it is much smaller than that epsilon that we then chose.

They say it's an anomaly and also something unnatural, but they don't know that homosexuality is documented in detail in 500 species and it was registered in 1500.

In the next video, I'll talk a bit about the Gaussian distribution and review properties of the Gaussian probability distribution, and in videos after that, we will apply it to develop an anomaly detection algorithm.

What the anomaly detection problem is, we want to know if this aircraft engine is anomalous in any way, in other words, we want to know if, maybe, this engine should undergo further testing because, or if it looks

And so, having built a model of the probability of x we're then going to say that for the new aircraft engine, if p of x test is less than some epsilon then we flag this as an anomaly.

So in his kitchen he was in there with his wife, Judy, and his bodyguard, Sean and I said, You know how I said in my email that you might have a special brain anomaly that makes you special?

Why this is interesting is because in the past decade we have found that this is not just some anomaly of this ridiculous, glow in the dark bacterium that lives in the ocean all bacteria have systems like this.

As an intellectual matter, looking at the world from other species' points of view helps us deal with this weird anomaly, which is and this is in the realm of intellectual history which is that we have this Darwinian revolution 150 years ago ...

So we see a new engine that, you know, has very low probability under a model p of x that we estimate from the data, then we flag this anomaly, whereas if p of x test is, say, greater than or equal to some small threshold.

Perhaps the most common application of anomaly detection is actually for detection if you have many users, and if each of your users take different activities, you know maybe on your website or in the physical plant or something, you can compute features of the different users activities.

So, p of x is equal to this product, and what you implement, what you compute, is this formula and where over here, this thing here this is just the formula for the Gaussian probability, so you compute this thing, and finally if this probability is very small, then you flag this thing as an anomaly.