So Katie this is about Enron.
Tell me, what is the Enron story?
>> Oh, the Enron story is so interesting.
The Enron story is the largest case of corporate fraud in American history.
>> What happened? >> That's what it is.
So Enron was an energy trading company, that, in around the year 2000 or
so, was one of the top ten largest companies in America, huge company.
They had their fingers in all kinds of different projects,
and by the middle of 2002, it was gone.
It was bankrupt, there were dozens of people in jail, there was billions of
dollars, tens of thousands of people's jobs that were, poof.
And as you might suspect when something like this happens, a multibillion dollar
company just collapses, it didn't happen totally organically.
There was a lot of fraud that was going on underneath the surface.
In some ways, it was amazing that the company didn't go bankrupt even
earlier than that, considering how much fraud was going on.
>> Well, that's, that's super interesting.
So why is it relevant for this class?
>> It is relevant for this class because, as it turns out, Enron has had, sort
of, a second life in the form of one of the most famous data sets of all time.
And the data set that we'll be using, sort of,
to motivate a lot of our projects in the rest of this class.
Its called the Enron Corpus, and its tens of thousands of emails.
Hundreds of thousands of emails.
>> So real emails by real people?
>> Real emails by real people.
It's one of the, as far as I'm aware,
the biggest corpus of emails that sort of exist in the wild.
>> That exist in the open, I guess.
>> In the open, that's right.
Because emails are usually so confidential, right?
And so, it's been used for many, many different purposes.
And we'll be using it to answer a very interesting question in this class.
I want to use this corpus to try to figure out if
there are patterns within the emails of people who were persons of interest in
the fraud case to see if you can identify those patterns.
>> So these are likes real emails for real people-
>> Yeah. >> And if they have an affair, you'll find it in the email and
their misappropriate money, you'll find it in the emails.
>> Yes, yes.
>> So you're going to see all this.
>> Oh, you can, you see everything.
In fact, one of the things that was very interesting to me as I
was learning about sort of the Enron fraud case.
And I was reading newspaper articles and watching, watching documentaries.
You get a sense for some of these individuals and
exactly the roles that they were playing in this fraud.
And as you go in and actually read their emails is a very weird experience.
So, yes, it's, it's real.
>> So that's the case of the past, right?
But this machine learning is about the future.
Why is it interesting to go this email corpus and find panels of fraud?
Like who would care?
>> The thing that I love about this project is while this data set has been
out in the world and very famous for the last decade or
so, no one's ever actually tried to answer this particular question.
Can we identify patterns in the emails of people who were-
>> Who would care about this?
The NSA, are you working for the NSA?
>> I care about it, I think it's a really interesting question.
>> That's the thing is it,
is it, this is at least, for my case, it's a, it's a case of curiosity.
Of course, if you were a some kind of forensic, machine learning expert,
you might, this might actually not be a, a case of the past for you.
You might be really interested in trying to answer this question and
might have real world implications.
For me, I'm just, I'm just perversely curious.
>> Interesting.
So Katie, you've done experiments on the data already.
>> I've done a lot, yes. >> On your own so-
So what do you do, what worked, what didn't work?
>> Well, that's basically going to be what I'll be teaching for
the rest of the class.
I'll be, a lot of the projects all of the mini projects,
in fact, are going to be coming back to this Enron corpus.
As I'm trying to understand things about it.
And using different machine learning techniques to attack it
from different angles.
I've done a lot of smart things, and I've done a lot of very stupid things, and
I've learned in the process of, of, this this exercise.
So one thing that I will be doing also from here on out is pointing out some of
the places where I've done something that, that I didn't realize at first but
was kind of stupid.
And how I figured out what my mistake was and how I caught it.
So my hope is that this is a little bit of
watching a machine learning exercise in practice.
That in some ways, I am two steps ahead of you, kind of macheteing my way
through the jungle, but that the students are there, one step behind me.
And they're sort of watching my thought process as its unfolding.
As I'm trying to answer this question.
>> What do you think the students will learn from this data,
and where do you think they can apply it?
>> It's an incredibly rich data set, and there's a lot of
places where we'll be talking about things that have broad application.
So the things we'll be addressing in this coming few lessons, for
example, we'll be talking about regressions.
So using regressions, I was able to understand the relationship between
the salaries of the people in Enron and their bonuses.
And see if there are any interesting relationships there.
As it turns out, I'm not sure that it has a lot of implication for
the fraud, but it was something that I found very interesting.
We'll be looking at clustering.
You can use clustering on the data which is a, a type of unsupervised learning.
It's a very interesting algorithm to figure out who within this
within this organization was a member of the board of directors and
who was just a regular employee.
We'll be using outlier detection and
removal to find certain lines in the data set that were bugs basically.
That were outliers, that I had to go in and clean out manually.
To make sure that we were getting sensible results from our data.
Stuff like that.
>> Great, great.
And students will they basically be watching you do this?
Or will they do it themselves?
>> A little bit of both.
There are places where there's something that I would wrestle with for
many days at a time before I finally figured out what was going wrong.
So my hope is that they will not be wrestling quite as hard that,
as I've learned some of these things, I can point them in the right direction.
But for the most part,
it will be replicating the steps that, that I've been following and
coming up with the answers to the questions that I've been curious about.
>> And in terms of the skills of student learn by doing this,
the skills that they can take on in their professional lives.
Like, what are other examples?
Because the Enron data set is like, one in a decade.
And it's a specific one that was made public for some specific legal action.
>> Right.
>> Like, what other things, today, can the students use it for,
once they've taken this specific example?
Well, it's, a lot of the topics that we'll be covering will
be using Enron as a use case.
But of course, that's not the only use case for any of these things.
So let's use the example of clustering.
So I said that clustering will be using to identify the difference
between people on the board of directors and ordinary employees at Enron.
But clustering is something we have an interview with a vice president in
Netflix, who is very involved in their machine learning.
And in Netflix, what they do with clustering is they actually identify
particular types of people by their movie choices.
And so they can have clusters of users that they say, here's, here's the,
whatever 25 to 35 year old men who like to watch action movies and comedies.
Here's the children who like to watch Disney movies.
Here's the women who like to watch romances and these, based on these clusters,
they can figure out who their, yeah, who their customers are.
>> So you're telling us by analyzing the email of a fraudulent organization and
then many years ago, you learn about movie preferences of people in Netflix?
>> By using the same algorithms, yeah, it's not too,
it's not too much of a stretch.
because there are many different questions that you can be asking, and
machine learning is a tool that you're going to be using to answer them.
So whichever, whichever direction you point that fire hose in is-
>> That, that, that's really exciting.
>> I'm, I'm very excited about this.
And there's one thing that I, I do want to emphasize.
Which is that what we're trying to do with this data set identifying persons of
interest in the fraud, it, it hasn't ever been done before, as far as I'm aware.
And we picked it because I think it's a really cool problem.
Not because I know that there's an answer that's easy to find.
And so I, I think it's very important that that point be
made because it's there's lots of things out there that would be easy to solve.
And this isn't one of those cases, and in fact, at the end if there's a student
who gets very into this and really starts to dig into the data on their own,
it can very easily become more of an expert than, than I am right now.
So I really hope that there are students out there who get
as excited about this as I am and, and make it their own project,
start to engineer their own features, come up with their own ideas, and
figure out how to implement them because while I'm setting a good example,
I'm getting you off to a good start, there is no way that I'm covering
all the smart ideas that could solve this, right?
>> This is great.
Thank you, Katie.
>> Thank you.