Computational Linguistics, Analytics, Search and Informatics
Lucile Berkeley Buchanan Building, rooms 114 & 124
T: 303-492-2159
clasic_contact@colorado.edu
This unique interdisciplinary degree provides a solid foundation in both computer science and linguistics as well as in current methods used in natural language processing and artificial intelligence. CLASIC training is aimed at preparing students for careers in language modeling, automatic question-answering, machine translation and interactive virtual agents.
Distance Education Option
Students can take individual courses toward a master's degree or graduate certificate through distance education (online). For more information, connect with the individual graduate program directly.
Due to the hands-on learning experience, some courses for the CLASIC degree must be taken on campus. This program cannot be completed entirely through distance learning.
Bachelor's–Accelerated Master's Degree Program
Students may earn this degree as part of the Bachelor's–Accelerated Master's (BAM) degree program, which allows currently enrolled CU Boulder undergraduate students the opportunity to earn a bachelor's and master's degree in a shorter period of time.
For more information, see the Accelerated Master's tab for the associated bachelor's degree(s):
Requirements
Students must complete at least 32 hours of approved graduate study, including a 2-credit capstone course focused on a publishable research project, which will run in conjunction with an internship or a CU-based research project. As part of the capstone, students will be evaluated by their employer or industry project manager. Students will also prepare a technical report on the completed project that the program directors and project leader will jointly evaluate. A minimum course grade is a B and a minimum GPA for graduation is a 3.0.
To fulfill core requirements defined below, students must take graduate breadth courses in 3 different breadth bins. This includes core computer science (bins 1 and 3) and core CLASIC (bin 2).
Required Courses and Credits
| Code | Title | Credit Hours |
|---|---|---|
| Core Linguistics Courses | 9 | |
| Choose two of the following: | ||
| Linguistic Phonetics | ||
| Morphology and Syntax | ||
or LING 6450 | Syntactic Analysis | |
| Semantics and Pragmatics | ||
| Choose one: | ||
Any LING course at the 5000-, 6000- or 7000-level (subject to advisor approval) | ||
| Core Computer Science Courses | 6 | |
| Bin 1 (choose one) 1 | ||
Recommended options: | ||
| Design and Analysis of Algorithms | ||
or CSCI 5444 | Introduction to Theory of Computation | |
or CSCI 5714 | Formal Languages | |
| Principles of Numerical Computation | ||
or CSCI 5646 | Numerical Linear Algebra | |
| Bin 3 (choose one) 1 | ||
Recommended options: | ||
| Datacenter Scale Computing - Methods, Systems and Techniques | ||
| Object-Oriented Analysis and Design | ||
| Fundamental Concepts of Programming Languages | ||
| CLASIC Capstone | ||
| LING/CSCI 5140 | CLASIC Capstone | 2 |
| Core CLASIC Courses | ||
| CSCI/LING 5832 | Natural Language Processing (Required for everyone. Satisfies Bin 2 requirement) | 3 |
| Choose two of the following: | 6 | |
| Current Topics in Computer Science (Topics: Computational Lexical Semantics or Computational Models of Discourse) | ||
| Computational Phonology and Morphology | ||
| Choose two of the following: | 6 | |
| Network Analysis and Modeling | ||
| Data Mining | ||
| Machine Learning | ||
| Neural Networks and Deep Learning | ||
| Advanced Machine Learning | ||
| Current Topics in Computer Science (Inference, Models & Simulation for Complex Systems) | ||
| Topics in Nonsymbolic Artificial Intelligence (Probabilistic Models of Human & Machine Intelligence) | ||
| Topics in Nonsymbolic Artificial Intelligence (Representation Learning for Language) | ||
| Introduction to Computational Corpus Linguistics | ||
| Open Topics in Linguistics (Machine Learning and Linguistics) | ||
| Topics in Language Use (Formal Models of Linguistics) | ||
| Topics in Comparative Linguistics (Computational Grammars) | ||
| Topics in Logic | ||
| Modal Logic | ||
Any other CSCI or LING course at the 5000-, 6000- or 7000-level | ||
Any Core course listed above (not already taken) | ||
| Total Credit Hours | 32 | |
| 1 | Visit the computer science department website for a full list of course options in each of the 3 breadth bins. (Updated every two years.) |
Learning Outcomes
The program is intended to:
- Provide a solid foundation in computer science, data-driven linguistics and natural language processing graduate coursework.
- Educate graduates to be specialists in the application of computers to the processing of natural languages, from major world languages like English, Chinese, Arabic and Urdu to low-resource languages, such as those spoken by indigenous peoples around the world.
- Prepare students for jobs in natural language processing, text analytics and artificial intelligence, fields critical to the success of mainstream global businesses who compete for qualified employees.