A worked example for LDA: Initial setup

The clustering model inherently assumes that data divide into disjoint sets, e.g., documents by topic. But, often our data objects are better described via memberships in a collection of sets, e.g., multiple topics. In our fourth module, you will explore latent Dirichlet allocation (LDA) as an example of such a mixed membership model particularly useful in document analysis. You will interpret the output of LDA, and various ways the output can be utilized, like as a set of learned document features. The mixed membership modeling ideas you learn about through LDA for document analysis carry over to many other interesting models and applications, like social network models where people have multiple affiliations.<p>Throughout this module, we introduce aspects of Bayesian modeling and a Bayesian inference algorithm called Gibbs sampling. You will be able to implement a Gibbs sampler for LDA by the end of the module.

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