Topic Modelling

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• Dr Tomoharu Iwata (NTT)
• Thursday 29 March 2012, 14:00-15:30
• Engineering Department, CBL Room 438.

If you have a question about this talk, please contact Konstantina Palla.

A topic model is a probabilistic generative model for extracting a latent structure from discrete data such as text document. Topic models are successfully used in a wide variety of applications including information retrieval, collaborative filtering and image recognition. In this talk, first I will present basics of topic modelling, such as relations with other probabilistic models, and inference. Then, I will present three applications of topic modelling: visualisation, social annotation data analysis, and multi-scale dynamics analysis, which I worked on recent years.

This talk is part of the Machine Learning Reading Group @ CUED series.

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