[MUSIC] Hi.
I'm Carlos Guestrin. I'm the Amazon Professor
of Machine Learning. >> Wait, I'm the Amazon Professor
of Machine Learning. >> Who are you? >> I'm Emily Fox. >> Well, I'm the Amazon Professor of
Machine Learning in Computer Science. >> And I'm the Amazon Professor of
Machine Learning in Statistics. >> We're both at the University
of Washington in Seattle, this is the United States. [LAUGH] This is. >> This is the United States of America. >> Of America. >> To clarify. >> America. >> America. >> [LAUGH] So what are your
qualifications to be here today, Emily? [LAUGH]
>> Am I supposed to read my CV? No, okay, just the beginning of it. What are my qualifications? Okay. Well, I went to MIT for way too long,
nine years, though it wasn't all for one degree, got a couple of them
[LAUGH] during those nine years. But yeah, I was there for my PhD studying in electrical
engineering and computer science. Should I list more qualifications? I think that's pretty good. >> She's very well qualified. Let me just say that. [LAUGH] And I got my undergrad from
University of Sao Paulo in Brazil. And my PhD from Stanford
in computer science and I've been working in machine learning for
a long time, as Emily likes to say. >> A very long time. I guess maybe I should describe a little
bit about my transition to statistics. So, after I did my PhD in
electrical engineering and computer science,
I did a postdoc in statistics at Duke, and actually, honestly, a lot of my
PhD really was a statistics PhD. But MIT, as of the time of this recording,
does not have a statistics department, and did not at the time I studied there
so, yes, I'm a self-taught statistician. >> And we can all be self taught. >> Yes.
>> [LAUGH] >> That's what this MOOC is about. >> [LAUGH] Hey, we're learning together. So I've been working machine learning for
awhile and I'm really excited about methods in machine learning
from a wide range of settings. I've worked on theoretical algorithms for
what's called planning under uncertainty for making decisions
using machine learning. I've worked with sensor data
with distributed sensors, something called sensor networks which
today is called Internet of Things. But this is way back and then I took a big
focus on large-scale machine learning. So how do you scale up machine learning
methods to lots and lots and lots of data? And we'll use some of these ideas
in this specialization as well. How about you? >> I don't have quite as long
of a history as Carlos does. [LAUGH]
But the things that I really enjoy working on are time series applications,
the data that comes in over time and we want to find some underlying
structure or patterns to that data. And a lot of my focus in my research
is on how to scale up to really high dimensional or
large collections of time series, especially when they have
very complex dynamics. And another one of my
big interests is how to make inferences online
as data is streaming in. And I've worked on a wide range
of different applications, some that I'm really passionate about. One in particular, right now,
is neuroscience, but I've also worked on speech data,
motion capture applications, analyzing human motion, to financial
data and the list goes on and on. But lots of data have really
important temporal structure and that's really a passion of mine. >> Yeah.
>> No, yeah, that's it. >> And we've been teaching machine
learning for a long time and maybe in the university setting-
>> Some of us longer than others. >> This joke never ends. >> No.
>> And many at the university setting. So here we are,
it's an exciting experience for us to teach in this online course. >> Yes. >> So how did you decide
to do this online course? >> How did I decide? That's a very good question. Well, no, there are a lot of reasons
why I wanted to do this online course. But really, the most fundamental reason
was thinking about how to disseminate machine learning to a very broad audience, because personally, I came from a very
different background and training. And actually Carlos did as well and
so I, myself, have gone through this experience of trying to
learn machine learning, learn statistics. And I think there's a really nice way that
it can be taught at a very fundamental level to a very broad audience and
that's one of my passions. And another reason is I'd like to think
about this for other goals in the future of how to convey this to other groups that
I interact with on campus as a teacher at the University of Washington,
professor [LAUGH], assistant. [LAUGH]
>> Not for long. So, yeah, and I'm very excited about
how machine learning gets taught. And we really have been thinking
a lot about how to approach this really broad idea of
machine learning in a new way. So in this specialization, as we'll talk more about in this module,
we'll experience machine learning in a whole different way than
you experienced in other places. We're really gonna be focused on for
applications first, understanding how machine learning
has impact and then digging in and understanding how those methods
are built and how they can be useful. So part of the journey for us is to build a new way to teach
machine learning, which is cool. >> It is cool. >> So we're excited. >> Yes. We're excited. [LAUGH] [MUSIC]