Here are some highlights from the Linguistics department's Spring 2022 offerings:
Credit: 3 hours.
Introduction to the role of language in globalization by examining communication issues concerning language use across cultural, political and geographic boundaries. Explores the interaction of language and other cultural forms in the global context. Among the topics discussed are issues of identity, spread of English and its acculturation to local contexts of use, creativity in language mixing, language in global pop cultures, language in cyberspace, as well as minority language experiences, and loss of indigenous languages.
This course satisfies the General Education Criteria for:
Cultural Studies - Non-West
Credit: 3 hours.
Introduction to the theory and methodology of psycholinguistics with emphasis on language acquisition and linguistic behavior. Covers the main questions in psycholinguistics and surveys what experimental designs are available to study the psychology of language. Topics covered include child language and bilingualism, second language in the brain, speech production and perception, word and sentence processing, and research design.
This course satisfies the General Education Criteria in Spring 2022 for:
Social & Beh Sci - Beh Sci
Credit: 3 OR 4 hours.
Prerequisite: LING 402 or equivalent.
Provides an elementary introduction to concepts, principles and algorithms of digital signal processing. It focuses on computational implementations of contemporary methodologies in digital signal processing rather than underlying mathematical theories, and therefore requires students to have basic Python or MATLAB programming skills as prerequisite. This course comprises lectures and laboratory sessions, during which students will experiment with producing their own computer code aided by ready-made programs to solve practical problems.
Credit: 4 hours.
Prerequisites: LING 406 and an introductory level Computer Science programming course, or consent of instructor.
Section E: Introductory Machine Learning
Machine learning has been thriving in many areas for both research and industry. It offers solutions to problems that traditional approaches may not be able to deal with or fall short in efficiency. From supervised to unsupervised learning, this course is set to give students a broad understanding in modern machine learning methods and techniques. During the course, students are expected to acquire knowledge and skills in solving practical problems in clustering and classification, using techniques such as k-means, Gaussian mixture models, decision trees, support vector machines and neural networks. This course requires LING490 or equivalent in programming and understanding in probability and statistics as prerequisite.
Section GDP: Computational Semantics
This course will examine how computational tools and models helps us advance our understanding of cutting-edge topics in the study of meaning in natural languages, including the semantics of logical terms and phrases, various kind of logical and probabilistic inferences, and the interface between language and general cognition. It will include a preliminary introduction to lambda calculus, compositionality and higher-order logic. We will then discuss the semantics of various functional and logical terms and phrases of natural languages---quantifiers ('most', 'some'), connectives ('and', 'or'), modals ('possible', 'must', 'likely'), polarity-sensitive items ('any')---and the semantics of questions, question-answer systems, discourse representation, selection of variables, and logical, probabilistic and pragmatic/common-sense inferences in natural languages. We will also introduce some standard and more recent computational models of the open class lexicon (words like 'table', 'tigers') and discuss the ways in which recent computational models of the lexicon advance our understanding of the interface between language and world knowledge/general cognition.
For a full listing of the department's course offerings, please view our course catalog.