So in this lecture, we will be summarizing what we learned in this section, this section, which is
part of our module on machine learning, looked at topic modeling as you saw topic modeling as an unsupervised
method, which is similar to clustering.
We learned about two algorithms that could be applied to this task, namely latency, richly allocation
and non-negative matrix factorization.
LDA is quite a complex algorithm, but it was helpful to see it from the perspective of inputs and outputs.
If you were more advanced, then you also learned a little bit about graphical models and how LDA assumes
documents are generated.
One major difference between LDA and a simple mixture model is that a new topic is sampled for every
word.
In a simple mixture model, a topic would only be sampled once.
We also looked at non-negative matrix factorization, which is an algorithm derived from recommender
systems.
In fact, we started this lecture by looking at recommenders to better understand the motivation behind
the matrix factorization approach.
We then realized how this approach could be immediately applied to topic modeling, since the model
parameters happen to have the same format as the outputs of LDA.
In addition, we also noted that we could go in the reverse direction and apply LDA to recommender systems
as well.