So what we're gonna talk about in this course is how intelligent behavior can
come to the web, using lots of big data. In short, how to predict the future using
artificial intelligence techniques and big data.
We'll study a number of elements of AI technologies in this course, organize
along the lines of the look, listen, learn, connect, predict, correct cycle.
And briefly, looking is about finding stuff and searching.
Listening is about figuring out what's important, what is not and classifying
things and clustering things together, which is all machine learning.
Learning is actually about. Extracting knowledge, and facts.
From data. Reasoning is about, putting.
Different pieces of fact, of information and facts together to draw further
conclusions. Prediction is about.
Mining rules and associations from data. And finally, of course, optimization is
about figuring out the right thing to do given all the predictions that one could
muster. We won't be talking about optimization
much in this course. But we will be talking about pretty much
all the other things to a certain extent. Along the way, we will also talk about big
data technology and I included an extra element called load where we figure out
how to harness such technologies using parallel programming and MapReduce.
Now this is a fairly vast material to cover.
So there's a caveat here. There's a vast amount of material to
cover. This is a graduate level course.
At the same time, it's not a full course, at IID it's a one credit course and at
IIIT it's a two credit course, and there will be some extra work for IIIT students.
And as I just mentioned the range of topics is vast.
While they are all closely related. That is the look listen, cycle.
But it's still large, so what we'll do is. Introduce each element, with the.
An example. Carefully in detail to give a feel for
that topic, without covering it in it's entirety.
And it also, at the same time outline the challenges and research problems in that
area so that graduate students can learn something and hopefully get pointers to
areas to work on in research. Some miscellaneous items.
Before we, start. Basic programming, some SQL and
data-structures, understanding what it means for an algorithm to be order-n,
order-n square, these things I assume you know.
Basic exposure to probability, statistics and understanding what matrices and
vectors are to a basic level, that also is assumed.
So if you don't know any of this and are...
But are confident to figure it out, go ahead and join but otherwise, be warned.
Evaluation, online quizzes, homeworks and programming will be about 60 percent of
the course. The final will be 40%.
At I, I T deli and triple I T. This would be an in class physically.
Co-located final, but online, so they will be making arrangements for you to take
this in front of computers by sitting inside a single room.
For the rest of you, it will be online and at your leisure.
Last but not least, the discussion forum is a very important part of this course.
So please join and participate, share your thoughts, your suggestions, your feedback
and engage in animated discussion in all topics.
To the course as well as otherwise. Most importantly, please respect the honor
code. In particular, it's okay to discuss
homework not to share the answers. Don't reveal the mystery's end before
someone else has started reading the first page.
Nobody likes that.