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. The minimum course grade is a B and the minimum GPA for graduation is a 3.0.
Choose from graduate breadth courses offered in the three different breadth bins. CSCI/LING 5832, Natural Language Processing (NLP) in Bin 2 is required as a Core CLASIC course; one course from Bin 3 is required; the third course can be from any of the bins.
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 1 | ||
| Recommended options: | ||
| Design and Analysis of Algorithms | ||
| Probability for Computer Science | ||
| Bin 2 | ||
| Recommended options: | ||
| Machine Learning | ||
| Data Mining | ||
| Fundamentals of Neural Networks and Deep Learning | ||
| Bin 3 | ||
| Recommended options: | ||
| Datacenter Scale Computing - Methods, Systems and Techniques (Bin 3) | ||
| Object-Oriented Analysis and Design | ||
| Fundamental Concepts of Programming Languages | ||
| CLASIC Capstone | ||
| LING/CSCI 5140 | CLASIC Capstone | 2 |
| Core CLASIC Courses | 15 | |
| Natural Language Processing (Required for everyone. Bin 2 course.) | ||
| Choose two of the following: | ||
| Computational Phonology and Morphology | ||
| Computational Lexical Semantics | ||
| Computational Models of Discourse and Dialogue | ||
| Choose two electives from the folowing: | ||
| Network Analysis and Modeling | ||
| 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
By the completion of the program, students will be able to:
- Demonstrate skills in computer programming, quantitative analysis, data management and processing, statistical machine learning and the development and deployment of large language models.
- Understand the implementation of statistical machine learning and the development and deployment of large language models.
- Identify major concepts of linguistics, including both the structures of language and the social context of language use and variation, and apply those concepts to building better and fairer applications.
- Analyze and develop solutions to real-world problems in the field of computational linguistics, also known as text analytics, natural language processing and informatics.
- Communicate knowledge in the discipline through writing and oral communication.