EN 600.405 - Applications of Probabilistic Models to Language and Speech Processing

Institution:

Johns Hopkins University

Subject:

Description:

Applications of Probabilistic Graphical Models in Language and Speech Processing Probabilistic graphical models (PGMs) combine ideas from statistics and computer science into a unifying framework for modeling complex real-world phenomena. PGMs are now widespread in language and speech processing. PGMs are well suited to handle the inherent challenges of linguistic problems: complex and structured relationships, a large number of relevant attributes, and large volumes of data. This short course will provide students with advanced training in several specific applications of graphical models that are important in natural language processing. After reviewing the essentials of directed and undirected graphical models, we will discuss complex CRFs, approximate inference including variational and MCMC methods, Bayesian models and non-parametric Bayesian models including Chinese Restaurant Processes. Students will also gain practical experience by solving problems using existing PGM software. Recommended Prerequisite: 600.465. Short course.

Credits:

1.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

www.jhu.edu

Phone Number:

(410) 516-8000

Regional Accreditation:

Middle States Association of Colleges and Schools

Calendar System:

Semester

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