Henriapi illustrates this phenomenon as well.

Only instead of, you know, good and bad cancer tumors or benign or malignant tumors we instead have diabetes or not and so we will use that as a supervised, we will solve that as a Supervised Learning problem just like we did for the breast tumor data.

So on. So this example...

We didn't sequence his genome.

So 0 may denote the benign tumor and 1 positive class may denote a malignant tumor.

Here's an example of a training set for a classification task for classifying a tumor as malignant or benign and notice that malignancy takes on only two values zero or no or one or one or yes.

So, if angiogenesis is a tipping point between a harmless cancer and a harmful one, then one major part of the angiogenesis revolution is a new approach to treating cancer by cutting off the blood supply.

Walt, your tumor has shrunk by 80 percent.

That's another classification problem, and earlier we also talked about the example of classifying tumors as a cancerous malignant or as benign tumors.

What does that mean?

That's counterindicative for a hemorrhagic tumor.

Let's see you want to look at medical records and try to predict of a breast cancer as malignant or benign.

Mice with multiple myeloma, an incurable malignancy of the bone marrow, respond dramatically to the treatment with this drug.

And bone marrow is actually what we use to save the lives of tens of thousands of patients, most of whom have advanced malignancies like leukemia and lymphoma and some other diseases.

A few years ago I felt a lump on the side of my neck, and it was my turn in the MRI machine.

Tumors?

Chronic viral ulcerative lesion.

And a different set of patients, they look a little different, whose tumors turn out to be malignant, as denoted by the crosses.

In other words, I'm going to tell my patient that the tumor, sadly, has a 70 chance or a 0.7 chance of being malignant.

So given a data set like this, what the learning algorithm might do is throw the straight line through the data to try to separate out the malignant tumors from the benign ones and, so the learning algorithm may decide to throw the straight line like that to separate out the two classes of tumors.

The cancer.

So this is a photograph of Henry, two weeks after he had a malignant cancer removed from the left side of his face his cheekbone, his upper jaw, his eye socket.

If someone discovers a breast tumor, a lump in their breast, a malignant tumor is a tumor that is harmful and dangerous and a benign tumor is a tumor that is harmless.

This is a little Bangladeshi girl from the east end of London who's got a huge malignant tumor on the right side of her face, which has already made her blind and which is rapidly growing and is going to kill her shortly.

But often there is this intuition that the negative class is conveying the absence of something, like the absence of a malignant tumor, whereas one, the positive class, is conveying the presence of something that we may be looking for.

I won't go into this very much, but our technology, besides being able to tag tumor and metastatic lymph nodes with fluorescence, we can also use the same smart three part molecule to tag gadolinium onto the system so you can do this noninvasively.

He died from acute lymphoblastic leukemia.

But inevitably, these tumors progress towards being enormous, ulcerating tumors like this one here.

So instead of drawing crosses,

Women suffer from that disease.

And one afternoon, I got a call from my doctor.

What is it? A tumor?

I wish I'd had conventional reconstructive techniques rather than this because I'm now dying of a malignant cancer ?

This one in particular sticks in my mind, because this is the first case of this disease that I saw myself.

But even as we grasp at victory, there is a cancer, an evil tumour, growing, spreading in our midst.

And started to use different symbols, circles and crosses, to denote malignant versus benign examples.

known as hibakusha , continue to carry they also suffer from numerous illnesses considered to be aftereffects of the A bomb

So let's say our data set looks like this where we saw a tumor of this size that turned out to be benign.

However, in the presence of the tumor, now there are molecular scissors that can break this molecule apart right there at the cleavable site.

One of this size, one of this size. And so on.

Spaceport floozy, spaceport floozy...

But even before I peel that muscle away, you saw that there was a tumor underneath.

We r emoved a tumor fr om her brain.

Now, in this example we use only one feature or one attribute, mainly, the tumor size in order to predict whether the tumor is malignant or benign.

You know, with this, hopefully you can decide that your friend's tumor is more likely to if it's over there, that hopefully your learning algorithm will say that your friend's tumor falls on this benign side and is therefore more