Computational Approaches to Human Language

Description from DukeHub

Natural Language Processing (NLP) has emerged as the cornerstone of generative artificial intelligence, bringing attention to a once niche domain. NLP applications are everywhere because language is everywhere. In this course we will explore and discuss a range of techniques designed to help machines perform tasks involving human language. This course follows the evolution of NLP from statistical approaches to deep learning based NLP, including a focus on large language transformer models. We will discuss modern developments and trends in NLP and LLMs. Students will learn about real world applications of NLP technologies and the complexities, ethics, and challenges faced in deploying them in enterprise scenarios.

Skills

• programmingdepartment: COMPSCI
• data and statisticsdescription: "statistical" and 1 more
• ai and machine learningdescription: "artificial intelligence" and 3 more

AI-resilient: 1 of 5 1/5

• No: Physical, lab or field work
• No: Live work with people
• ✓Yes: Judgment someone is accountable for · "ethics"
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

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All COMPSCI courses · All of Undergraduate