Spam

Who programmed the abbreviations? I did.

Spam Handling

So the dictionary might be very, very large.

Ham

The assignment of the 2 classes, you know, spam, no spam, and so on the assignment of the 2 classes to positive and negative, to 0 and 1 is somewhat arbitrary and it doesn't really matter.

But we had learned some other tricks along the way.

First I must solicit your confidence in this transaction, this is by virtue of its nature being utterly confidential and top secret...

Classify as Spam

How did we get this?

Y that we can think of as taking on two values, either zero or one, either a spam or not spam, fraudulent or not fraudulent, malignant or benign.

Classify as NOT Spam

Spam

And so we would just look for links that had 1 comment by the same author and mark it as spammers.

Ham

Most SPAM filters use human input.

This one is easy to calculate for SPAM and HAM.

Here are some examples of classification problems.

It's my turn. Noodles and Spam, okay.

And the correct answer is 23.

99 MlLLlON EMAlL ADDRESSES FOR ONLY 99

And users, legitimate users, rarely commented on their own link as the first comment.

Spam Status

Let's say k equals 1, and let's assume we get one message, and that message was spam, so we're going to write it one message, one spam.

If you have labeled data, you know, with spam and non spam e mail, we'd treat this as a Supervised Learning problem.

Why? Because I got a lot of spam.

Had an old Dell Dimension XPS sitting in the corner and decided to put it to use. And so on and so on.

I apologize to the other 3000 or so it was probably spam.

It's not 0.001. It's flat 0.

And again, it might look like spam because the word secret occurs.

Have you emailed this person before?

The final quiz, let's assume our message is Today is secret.

This form is easily transformed into this expression over here, the probability of the message given spam times the prior probability of spam over the normalizer over here.

And we figured if a link was good on reddit, that link should have some authority.

And just as before we count the word secret in SPAM and in HAM as I've underlined here.

Now the tricky part comes up over here.

Show spam status in fancy headers

It can't be that a single word determines the entire outcome of our analysis.

Sorry, no spam detection tools have been found. Install your spam detection software and re run this wizard.

For example, for the prior probability, we found that 3 8 messages are spam.

Yeah, I'm not sure if it's gonna be cool with them that I spam their emails.

Let's use this as an example to talk about machine learning for discrimination using Bayes Networks.

To see if we apply Bayes rule, which multiples the prior for spam ness with the conditional probability of each word given spam.

So, let's look at this a little bit more formally and talk about maximum likelihood.

like required email addresses?