So in this lecture, we'll be introducing the next section of this course, which is on topic modeling.
This lecture will outline what this section is about, and we'll also talk about why this subject is
relevant and useful.
So let's begin with a quick outline.
This section will cover two popular approaches to topic modeling latency richly allocation, also known
as LDA and non-negative matrix factorization.
Also known as an AMF, LDA is a very interesting machine learning method because, unlike the other
methods we've discussed, this is what we call a full Bayesian model of all the algorithms we learned
about in this course.
LDA is the most complex and requires the most background knowledge to fully understand.
Lucky for us, only intuition is required to understand the code.
But if you're interested in a full course that dives deep into this topic, I'd be happy to hear your
requests.
The second approach we'll discuss is an EMF, which originates from the field of recommender systems.
It turns out that because of the way it works, it can also be applied to topic modeling.
In fact, it's possible to sort of mix and match algorithms in applications.
So, for example, although LDA was initially built for topic modeling, we can go in the other direction
and apply this to recommender systems as well.
So let's talk about why topic modeling is useful.
One simple reason why it's useful in the context of this course is because it's an example of unsupervised
learning tasks like spam detection and sentiment analysis require labeled data sets.
But you'll see that topic modeling does not.
Thus, this presents a different paradigm for machine learning that we haven't yet encountered in this
course, and obviously not needing labels is pretty useful to.
Another reason it's useful is because it's almost like a more powerful version of clustering, as you
recall, clustering allows us to assign categories to our input documents, which is obviously useful
if you wanted to do something like organize your documents without having to manually label them yourself.
If you're a business that has to deal with many documents, this can save both time and money.
But unlike the discrete categories you get with clustering, you'll see that topics can be more richly
expressed.
Another application of topic modeling is document retrieval and search engines.
As you recall, one simple and straightforward method of doing this is to simply convert your documents
into RDF vectors and then do a nearest neighbor search to find the closest documents to your query.
However, this can be problematic.
For instance, if you're to fire, effectors are very sparse.
Topic modeling is a method of reducing a document into a small set of topics such that they can be more
easily and more accurately searched.