Topic modeling is a relatively recent and exciting area that originated from the fields of natural language processing and information retrieval but has seen applications in a number of other domains as well. Many problems in classification, such as sentiment analysis, involve assigning a single class to a particular observation. In topic modeling, the key idea is that we can assign a mixture of different classes to an observation. As the field is inspired from information retrieval, we often think of our observations as documents and our output classes as topics. In many applications, this is actually the case and so we will focus on the domain of text documents and their topics, this being a very natural way to learn about this important model. In particular, we'll focus on a technique known as Latent Dirichlet Allocation (LDA), which is the most prominently used method for topic modeling.