Carnegie Mellon University

Overview

The Master of Language Technologies (MLT) at Carnegie Mellon University is a comprehensive, research-oriented program designed for those seeking to deepen their understanding of language technologies. This two-year degree combines rigorous coursework with hands-on research, allowing students to engage directly with faculty and cutting-edge projects in the field.

What you'll study

The MLT program integrates advanced coursework with a strong emphasis on research, enabling students to explore various dimensions of language technologies. Throughout the academic year, participants will balance classes and research, dedicating summer months solely to their research projects under faculty supervision.

Typical coursework areas

• Grammars and Lexicons
• Algorithms for Natural Language Processing (NLP)
• Search Engines or Machine Learning for Text Mining
• Machine Translation
• Self-paced Lab (hands-on research and implementation practice)
• Directed Study (tailored to your research interests)

Research requirement

• Continuous faculty-advised research throughout the program
• Summer research terms focused on advancing individual projects
• Opportunities for academic presentations and publication of research findings, depending on project scope

Electives and specialisation

• Elective coursework designed to complement your research focus and enhance relevant skills
• Directed Study modules aimed at fostering expertise in areas aligned with your advisor’s lab and personal interests

Entry requirements

Candidates for the MLT program are expected to have a strong academic background, typically demonstrated by a minimum GPA. Additionally, applicants may need to submit standardized test scores and proof of English proficiency, depending on their educational background.

Career prospects

Graduates of the MLT program are well-equipped to pursue careers in various sectors, including technology, academia, and research institutions. With a solid foundation in language technologies, alumni often find opportunities in areas such as natural language processing, machine learning, and computational linguistics, making significant contributions to advancements in these fields.