Difference between revisions of "Class meeting for 10-605 LDA"
From Cohen Courses
Jump to navigationJump to search| Line 3: | Line 3: | ||
=== Slides === | === Slides === | ||
| − | * | + | * TBD |
| − | |||
| − | |||
=== Readings === | === Readings === | ||
Revision as of 16:57, 11 August 2016
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Fall_2016.
Slides
- TBD
Readings
- Distributed Algorithms for Topic Models, Newman et al, JMLR 2009.
- Efficient Methods for Topic Model Inference on Streaming Document Collections, Yao, Mimno, McCallum KDD 2009.
Things to remember
- How Gibbs sampling is used to sample from a model.
- The "generative story" associated with key models like LDA, naive Bayes, and stochastic block models.
- What a "mixed membership" generative model is.
- The time complexity and storage requirements of Gibbs sampling for LDAs.
- How LDA learning can be sped up using IPM approaches.
