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.