LDA is mostly known as a topic-modeling tool in information retrieval – assigning documents to latent themes. But its reach is much broader. Variants of the same generative logic power population assignment models in genetics (e.g., ancestry inference), mixture models in computer vision for scene classification, and many other advanced probabilistic systems.
What was missing was a single resource that connects all these perspectives: the statistical foundations, the intuition behind the generative process, and the algorithmic machinery that actually makes it work.
In this mini-series, I provide exactly that:
* A broad conceptual context – where LDA comes from and where it’s used
* Deep probabilistic intuition
* And the nitty-gritty implementation details – deriving the updates and coding both Variational Inference and MCMC from scratch (in Python)
If you’re interested in probabilistic modeling beyond black-box libraries, and want to truly understand what’s happening under the hood, this mini course is for you.
Curriculum
- 1 Section
- 4 Lessons
- Lifetime
