This is a series of lecture notes for probabilistic topic models written in ipython notebook.

The following metarials will be covered:

• Dirichlet distribution
• Latent Dirichlet allocation (LDA)
  • Gibbs sampling
  • Variational inference
• Dirichlet process
• Hiearchical Dirichlet process (HDP)

See lectures on nbviewer.com

• Lecture 0 - Preliminaries
• Lecture 1 - LDA with Sampling
• Lecture 1.5 - LDA with variational inference
• Lecutre 2 - DP with Sampling