Let's recap what we've learned this week.
First we spoke about search, indexing, and
ranking.
But if you will notice we.
Left out, an important part.
While alluding to, the difficulty.
Of, assembling.
Results, which are very large.
At the same time ranking them efficiently.
The homework problems.
Are related to this pro, this issue, and I
also expect that you'll have some animated
discussion on the forum on this particular
point.
At the same time, please don't give away
the exact answers to the homework
questions.
Though the discussions we will certainly
help all those having some difficulty with
the homeworks.
Next we discussed enterprise search and
searching structured data, we found that
it's not a solved problem and.
It's a little more difficult than
searching.
Web data for a number of reasons.
Finally, we turn to object search, and
covered a very important technique called
locality sensitive hashing.
We also briefly touched upon associative
memory, which is a related technique, but
doesn't actually store the objects.
Now if you think about the problem of
searching for objects, and instead of
actually wanting to find the objects which
are near your query, you only want to find
which set of objects this is likely to
belong to.
In some sense, you want to find the class
of objects, without worrying about the
exact objects that are similar to one's
query.
A bit like a associated memory.
Well, this is exactly the machine learning
problem.
And that's we will going to get into next
week, as you will find the approach that
we are gonna take for machine learning via
information theory, is slightly unique and
not covered in many normal machine
learning textbooks.
So, see you then and don't forget to
submit your homework by Monday.