IEEE SPS / UBC ICICS Summer School on Signal Processing and Machine Learning for Big Data

PROGRAM

In natural language processing, latent Dirichlet allocation (LDA) is a generative model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. For example, if observations are words collected into documents, it posits that each document is a mixture of a small number of topics and that each word's creation is attributable to one of the document's topics. LDA is an example of a topic model and was first presented as a graphical model for topic discovery by David Blei, Andrew Ng, and Michael Jordan in 2003.[1]

https://sites.google.com/site/s3pbigdata2014/program

IEEE SPS / UBC ICICS Summer School on Signal Processing and Machine Learning for Big Data

   

	Tuesday July 29	Wednesday July 30	Thursday July 31	Friday August 1
9:00-9:30	Opening ceremony
9:30-10:30	Konstantinos N. Plataniotis University of Toronto	Angshul Majumdar IIIT-Delhi	Ali Bashashti BC Cancer Agency	Ozgur Yilmaz University of British Columbia
10:30-11:00	Coffee break	Coffee break	Coffee break	Coffee break
11:00-12:00	Vikram Krishnamurthy University of British Columbia	Angshul Majumdar IIIT-Delhi	Tom Levi Plenty Of Fish	Michael Friedlander University of British Columbia
12:00-13:00	Lunch	Lunch	Social event with lunch	Rayan Saab University of California, San Diego
13:00-14:00	Georgios Giannakis University of Minnesota	Martin McKeown University of British Columbia
14:00-14:30	Coffee break	Coffee break
14:30-15:30	Georgios Giannakis University of Minnesota	Li Deng Microsoft Research