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So what we're gonna talk about in this
course is how intelligent behavior can

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come to the web, using lots of big data.
In short, how to predict the future using

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artificial intelligence techniques and big
data.

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We'll study a number of elements of AI
technologies in this course, organize

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along the lines of the look, listen,
learn, connect, predict, correct cycle.

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And briefly, looking is about finding
stuff and searching.

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Listening is about figuring out what's
important, what is not and classifying

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things and clustering things together,
which is all machine learning.

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Learning is actually about.
Extracting knowledge, and facts.

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From data.
Reasoning is about, putting.

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Different pieces of fact, of information
and facts together to draw further

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conclusions.
Prediction is about.

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Mining rules and associations from data.
And finally, of course, optimization is

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about figuring out the right thing to do
given all the predictions that one could

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muster.
We won't be talking about optimization

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much in this course.
But we will be talking about pretty much

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all the other things to a certain extent.
Along the way, we will also talk about big

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data technology and I included an extra
element called load where we figure out

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how to harness such technologies using
parallel programming and MapReduce.

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Now this is a fairly vast material to
cover.

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So there's a caveat here.
There's a vast amount of material to

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cover.
This is a graduate level course.

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At the same time, it's not a full course,
at IID it's a one credit course and at

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IIIT it's a two credit course, and there
will be some extra work for IIIT students.

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And as I just mentioned the range of
topics is vast.

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While they are all closely related.
That is the look listen, cycle.

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But it's still large, so what we'll do is.
Introduce each element, with the.

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An example.
Carefully in detail to give a feel for

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that topic, without covering it in it's
entirety.

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And it also, at the same time outline the
challenges and research problems in that

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area so that graduate students can learn
something and hopefully get pointers to

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areas to work on in research.
Some miscellaneous items.

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Before we, start.
Basic programming, some SQL and

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data-structures, understanding what it
means for an algorithm to be order-n,

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order-n square, these things I assume you
know.

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Basic exposure to probability, statistics
and understanding what matrices and

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vectors are to a basic level, that also is
assumed.

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So if you don't know any of this and
are...

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But are confident to figure it out, go
ahead and join but otherwise, be warned.

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Evaluation, online quizzes, homeworks and
programming will be about 60 percent of

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the course.
The final will be 40%.

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At I, I T deli and triple I T.
This would be an in class physically.

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Co-located final, but online, so they will
be making arrangements for you to take

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this in front of computers by sitting
inside a single room.

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For the rest of you, it will be online and
at your leisure.

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Last but not least, the discussion forum
is a very important part of this course.

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So please join and participate, share your
thoughts, your suggestions, your feedback

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and engage in animated discussion in all
topics.

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To the course as well as otherwise.
Most importantly, please respect the honor

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code.
In particular, it's okay to discuss

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homework not to share the answers.
Don't reveal the mystery's end before

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someone else has started reading the first
page.

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Nobody likes that.