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So the way we're gonna learn about this data to intelligence pipeline is by
examining a number of case studies that are gonna ground the methods that we
present in real world applications.
And that's one of the really unique features of this course.
In our first case study, we're gonna look at predicting house values.
So, the intelligence we're deriving
is a value associated with some house that's not on the market.
So, we don't know what it's value is and we wanna learn that from data.
And what's our data?
Well in this case, we're gonna look at other houses and look at their house
sales prices to inform the house value of this house we're interested in.
And in addition to the sales prices,
we're gonna look at other features of the houses.
Like how many bedrooms the houses have.
Bathrooms, number of square feet, and so on.
And what we're gonna do, our machine learning method is something
that's gonna relate the house attributes to the sales price.
Because if we can learn this model, this relationship from our house level features
to the observed sales price, then we can use that for predicting on this new house.
We take its house attribute and predict its house sales price.
And this method is called regression.
In our second case study, we're gonna explore a sentiment analysis
task where we have reviews of some restaurants.
So for example in this case, it says the sushi was awesome, the food was awesome,
but the service was awful.
And we wanna take this review and
be able to classify whether it had positive sentiment.
It was a good review, thumbs up or a negative sentiment, thumbs down.
And so how are we gonna do this?
Well, we're gonna look at a lot of other reviews.
So, we're gonna look at the text of the review and the rating of the review.
In order to understand what's the relationship here, for
classification of this sentiment.
So, for example in this case,
maybe we might analyze the text of this review in terms of how many times it uses
the word awesome versus how many times it uses the word awful.
And from these other reviews that we have, we're gonna learn some decision boundary
between based on the balance of usage of these words whether it's a positive or
negative review.
And the way we learn that from these other reviews is based on the ratings associated
with that text.
And so this method is called a classification method.
In our third case study, we're gonna do a document retrieval task where here,
what we wanna do, the intelligence we're deriving is an article or
a book or something like this that's of interest to our reader.
And the data that we have is a huge collection of possible articles that
we could recommend.
And what we're gonna do, in this case, is we're gonna try and
find structure in this data based on groups of related articles.
Such as, maybe there's a collection of articles about sports and world news and
entertainment and science.
And if we find this structure and annotate our corpus,
our collection of documents with these types of labels
which we don't have ahead of time, we're trying to infer this from the data.
Then we can use this for very rapid document retrieval because if I'm sitting
here currently reading some article about world news, then maybe, if I wanna
retrieve another article, I already know which articles to search over.
And this type of approach is called clustering.
In our fourth case study,
we're gonna do this really interesting thing that's called collaborative
filtering that's had a lot of impact in many domains in the last decade.
Specifically, we're gonna look at doing product recommendation,
where you take your past purchases and trying to use those
to recommend some set of other products you might be interested in purchasing.
So in this case, the data that we're gonna use to derive this intelligence for
product recommendation is we'd like to understand what's the relationship between
what you bought before and what you're likely to buy in the future.
And to do this, we're gonna use other users' purchase histories.
And possibly, features of those users.
But the key idea here is we're gonna take this data and
we're gonna arrange it into this customers by products matrix where
the squares here indicate products that a customer actually purchased.
So those are products that are liked by that customer.
And from this matrix, we're gonna learn features about users and
features about products.
And once we learn those features about users and
products from this data that I've described.
We can think about using those features to see how much agreement there is
between what the user likes, different attributes the user likes and
whether the product is actually about those attributes.
So in the example I'm showing here, maybe a user is a mom,
has certain features that are similar to other users that are also moms.
And from that, we can infer things about products.
What are attributes about?
For example, baby products that are of interest to moms.
And we're using that information to form our recommendations.
And this type of approach going from this matrix,
this customers products matrix into these learned features about users and
products is called matrix factorization.
Okay, well in our final case study,
we're gonna look at a visual product recommender.
So here, our data is somebody's gonna go to the web and they're gonna input,
not text, but an image.
They're gonna put an image like of a black shoe, or a black boot, or
a high heel, or some docker shoe, or running shoe.
And what they want is they want a set of results of shoes that
might also be of interest to them.
So, shoes that are visually similar to the picture that they have.
And they wanna be able to search over those to purchase this item.
And the way we're gonna do this,
to be able to go from an image to a set of related images is we need to
have very good features about that image to find other images that are similar.
And the way we're gonna derive those really detailed features is something
called deep learning.
So in particular, we're gonna look at these neural networks where every layer of
the neural network provides more and more descriptive features.
So in the little example we show here,
the first layer might just detect in the image things like different edges.
Whereas with one we get to the second layer, we start detecting corners and
more interesting features like that.
And as you go deeper and deeper in these layers,
you get more intricate features arising.
So as you see, we're gonna walk through a series of real-world case studies,
real-world problems and real-world solutions using machine learning.
And through this,
we're gonna explore a series of methods that have a lot of power out there.
And are gonna allow you to be able to develop and deploy new machine learning
techniques on new problems that aren't the exact case studies we used.
But the case studies will allow us to really ground the methods that
we're describing with things that are very interpretable.
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