This course gives a graduate level overview of concepts and techniques for statistical modeling of structured data. Much of the data we see is in an unstructured form â€“ text, images, videos, etc. How do we efficiently learn to extract structured information from such raw data? This could involve tasks such as parsing a sentence, creating a tabulated summary of the information in a webpage, adding tags to an image, recognizing objects in images, etc. The common thread across these applications is that predicting the output requires assignments to multiple interdependent variables. This course is an overview of topics in structured learning and prediction, with a focus on ideas that have emerged in the last couple of decades. We will look at several techniques for structured output learning and prediction using examples from natural language processing, computer vision and related areas. Students should be familiar with basics of machine learning. Enrollment Requirements: Prerequisites: CS 6350.