Our Master of Science (MS) in Computational Linguistics curriculum does an excellent job of preparing students for careers in speech recognition, artificial intelligence, machine translation, big data, automated text analysis and web search. The highlights of our curriculum include:
- Offering in-depth programming training so that students know how to build algorithms from scratch.
- Incorporating best practices for programming and cutting edge technology from the field.
- Through skill-building, our courses complement the learning process during on campus computational linguistics research work with faculty and off campus internships.
- Through the required internship, capstone project and/or thesis (our “exit requirement”), students gain invaluable work and independent research experience to add to their portfolios and to enhance their job or PhD application.
- Starting during orientation and throughout the program, you will benefit from tailored, one-on-one faculty advising to build a course plan that meets your needs.
The program requires students to complete at least 12 courses, which are a combination of core, background and elective courses. All students must take our 5 core computational linguistics courses. During orientation, students will meet with a faculty advisor to determine which of the six background courses (in linguistics and/or computer programming) will be required and to discuss elective options.
Students will satisfy the remainder of their 12-course requirement by taking electives and one "exit requirement" course. Representing a culmination of their learning in the program, the exit requirement allows students to use the skills they’ve developed in an internship, capstone project and/or thesis. Students enroll in a course in order to receive credit for the work in their internship, capstone project and/or thesis.
The First Year
The goal is for all students to emerge from the first year with:
- a strong foundation in the basics of both computer science and formal linguistics
- facility and comfort with the fundamental techniques, goals, and methodology of computational linguistics, natural language processing, and corpus linguistics.
- COSI 114a Fundamentals in Natural Language Processing I
- COSI 115b Fundamentals in Natural Language Processing II
- COSI 230b Natural Language Annotation for Machine Learning (formerly COSI 140b)
- COSI 10a Introduction to Problem Solving in Python
- COSI 12b Advanced Programming Techniques in Java
- COSI 21a Data Structures and the Fundamentals of Computing
- LING 120b Syntax I
- LING 130a Introduction to Formal Semantics
- LING 160b Mathematical Methods for Computational Linguistics
Any additional room in the first-year schedule is devoted to developing and strengthening the student’s computer programming abilities, along with taking other computer science or linguistics electives of interest to the particular student. Although not satisfying any requirements toward the MS degree, students can also opt to add courses of interest from other disciplines, such as foreign language study.
The Second Year
The goal in the second year is to build more advanced programming skills in preparation for the job market or PhD application.
- COSI 231a Advanced Machine Learning Methods for Natural Language Processing (formerly COSI 134a)
- COSI 232b Information Extraction (formerly COSI 137b)
- COSI 293b Internship or COSI 295a Capstone Project or COSI 299a Thesis
Additional advanced courses on applied or theoretically oriented topics within computational linguistics and natural language processing can include:
- COSI 112a Modal, Temporal, and Spatial Logic for Language
- COSI 132a Information Retrieval
- COSI 135b Computational Semantics
- COSI 136a Automated Speech Recognition
- COSI 138a Computational Linguistics Second Year Seminar
- COSI 216a Topics in Natural Language Processing
- COSI 217b Natural Language Processing Systems
- COSI 233a Discourse and Dialog
For more detailed program information, please review our Student Handbook and the University Bulletin. The most relevant sections of the Student Handbook for prospective students include: Degree Requirements, Course Selection, and the Exit Requirement.
The minimum residence requirement for full-time students is two years, i.e., four semesters of full-time enrollment.