Time: 12:45pm-1:45pm, Friday, October 23 Place: Room 4422, CUNY Graduate Center, 365 Fifth Ave (34str&35str). Speaker: Andrew Rosenberg (CUNY) Expectation Maximization Tutorial Abstract: Expectation Maximization (EM) is prevalent and powerful parameter estimation technique. In this tutorial, I will describe the basic structure of this iterative estimation technique. We will derive the mathematical foundation for fitting Gaussian Mixture Models. A brief survey of applications of EM in natural language processing will be presented. Time permitting, we will discuss the general framework that EM provides for estimating other parameters over other models involving latent variables. Bio: Andrew Rosenberg is an Assistant Professor at Queens College and CUNY Graduate Center. His research focuses on developing techniques for machines to process intonational information in speech. More broadly, his research is in the fields of Natural Language Processing, Spoken Language Processing and Machine Learning. He received his Ph.D. from Columbia University in 2009.