CS 598 JHM

Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

Topic: Advanced NLP: Theory and applications of Bayesian models. In recent years, Bayesian techniques have been applied to a number of natural language processing tasks. The aim of this course is to provide students with an understanding of the theory behind these models, and to enable them to apply these techniques in their own research. We will study Bayesian models such as Latent Dirichlet Allocation (topic models) and (Hierarchical) Dirichlet Processes and their applications to various natural language processing tasks. We will review both variational and sampling-based inference algorithms. The course will consist of a mixture of lectures and seminar-style presentations. A large component of this course will be a research project. Prerequisites: Machine learning (CS446), prior exposure to NLP (one of CS498, LING406, CS546) or approval of the instructor.