Welcome to the final lecture on Predict. This week, we'll start with bottom up
prediction. How we can predict future values of data
from the data that we have. We've seen some of this already in
learning. How we can predict what future instances
look like or are part of based on past experience.
But we'll go on to deal with predicting values rather than simply classes.
We'll begin with the very basic prediction technique, which is least-squares
approximation, and function approximation, in general.
Then, we'll go on to show the relationship between prediction, optimization, and then
controlling what you want to do with your predictions.
Next, we'll ask how the brain actually does all these things in a very smooth and
almost invisible manner and talk about some recent developments in something
called hierarchical temporal memory, which is a prediction system modeled after how
the brains and neurons actually are put together.
And it is the latest advance in what was originally the field of neural networks
but we'll see that neural networks, belief networks and everything is sort of coming
together in this fairly interesting development.
However, it turns out that bottom up prediction so far, can go only a certain
way and one needs to combine many different techniques including symbolic
reasoning as well as direct learning from the data.
And we will discuss a very popular and fairly old architecture called the
blackboard architecture which is becoming more and more important, as all these
techniques start working together in large complex systems.
Of course then, we'll finally summarize seeing where we've come in this course
about what we've learned about web intelligence, the brain, and adaptive
business intelligence based on all these techniques.
And lastly, I'll leave you with some challenge problems, which are fairly deep
and might interest some of you to actually take these up for research.