Welcome to week three, where we discuss
listening.
We all live in an ambient sea of data.
So whether or not we want to look for
anything, we need to process this volume
of data continuously bombarding us.
Nevertheless even as we walk around and
interact with our world we do get a sense
of things.
When walking into a new building, we sort
of get a smell of the place.
Is it a happy place?
Is it a very strict environment?
And many other such impressions naturally
form with our consciously wanting to
evaluate this.
Similarly we are able to easily recognize
the familiar, as well as the rare and
single it out for special detention.
What does all this have to do with the
web?
Now if you think about it web properties,
whether they are search engines, social
networks, auction sites, twitter,
virtually anything live on advertising.
As a result, they want to process this
large volume of data arising from our
interactions with them.
To discern our intents so as to target us
with the right messages from their
perspective and presumably from ours if we
find them useful.
So they want to do things like recognize
who is actually shopping from those who
are merely surfing, and treat the former
with a little more attention than the vast
majority who might not actually be
shopping for anything.
They want to figure out how to gauge our
opinions about products, services as well
as almost anything under the sun.
In short they want to understand what
people are saying or doing, much as in the
reverse tooling test we discussed last
week, so that they can target the right
advertising message to them.
So this week we'll study techniques that
allow these web properties, to learn
information about our intentions from
whatever they are able to measure about
us.
Learning information.
So we will study machine learning from the
perspective of information theory.