All are invited for the same *Title :* Tutorial on Structural Output Prediction *Venue :* KD 101 *Date|Day :* 29 Feb 2016 | Monday *Time : * 8:00 PM (IST) *Speaker* : Nitish Gupta *Abstract: * In binary or multi-class classification problems, the output variables are independent which allows us to accurately learn dependencies between input space and class labels with finitely many functions. Learning functional dependencies between arbitrary input and output spaces especially in problems involving multiple dependent output variables and structured output spaces is extremely difficult and cannot be achieved using trivial supervised learning algorithms for multi-class classification. In this talk, which will be more of a tutorial, I will start by giving a brief introduction to supervised methods for binary classification using linear classifiers and extending this idea to Multi-class classification. The focus in Multi-class classification will be on One vs. All, All vs. All, Multi-class SVM and Constraint Classification approaches. I will then introduce the problem of structured output prediction and present the various challenges it poses in training and inference. I will conclude the talk with a brief tutorial on a widely used supervised learning approach called the Structured SVM. *Bio: * Nitish Gupta is a Computer Science PhD Student at the University of Illinois, Urbana-Champaign and works under Prof. Dan Roth in the Cognitive Computation Group. Nitish's research interests lie in Natural Language Processing and Machine Learning, especially in large-scale machine learning models for Information Extraction. His research is geared towards extracting structured knowledge out of unstructured text, making textual world knowledge more accessible and informative. He is currently working on latent structure models for large-scale Cross Document Co reference Resolution/Entity Resolution.