As usual, let's recap what we've done this week and preview next week's lecture.
We focused on learning or extracting information from data such as figuring out
classes from data in an unsupervised manner using clustering. Figuring out
rules from data using unsupervised rule mining.
And we also took a look at big data, and how just counting techniques work well if
you actually have lots of data, and our unified f of x formulation helped up
understand clustering, rule mining as well as classification in the same single
formulation. We then turn to long data, high
dimensional data, and figure out how learn classes and features in an unsupervised
model using hidden or latent techniques. Next week, we'll continue our discussion
of learning a little bit because the techniques will be very similar to the
others that we'll talk about next week as well.
So, we'll discuss learning facts from collections of text via Bayesian networks
and hidden Markov models returning once more to supervised learning in a different
form, not just classification. And then, ask what use such rules and
facts are if in fact one doesn't believe John Sterling and one believes that one
can reason using rules and facts to connect the dots and make sense of the
world just the way we put two and two together.
We'll talk about logical, as well as probabilistic reasoning, reasoning under
uncertainty, and most importantly, the semantic web, where attempts have been
made to put all these reasoning techniques together in the context of data on the
web, extracting facts and reasoning about facts as being a higher order capability
that today the web doesn't have. But, might well do so in the very near future.
So, see you next week.