NLP Essentials
By Jinho D. Choi (2026 Edition)
Natural Language Processing (NLP) is a dynamic field within Artificial Intelligence focused on developing computational models to understand, interpret, and generate human language. As NLP technologies become increasingly embedded in our daily lives, understanding its fundamentals is crucial for both leveraging its potential and enhancing our interaction with language-based systems.
This course is designed to build a robust foundation in the core principles of modern NLP. We begin with text processing techniques that prepare and structure raw textual data for analysis. We then explore traditional language models that predict and generate text based on probability distributions, followed by large language models that enable natural conversation and complex reasoning. Vector space models introduce mathematical representations of documents for similarity and classification tasks. Finally, distributional semantics reveals how word meanings emerge from their contextual usage patterns in large text corpora.
The latter part of the course focuses on practical application through team projects. Students will have the opportunity to work with cutting-edge NLP technologies, such as large language models, to develop real-world applications. This hands-on approach encourages creativity and innovation, with students proposing their own ideas and presenting demonstrations to their peers.
Learning assessment combines concept quizzes to reinforce theoretical understanding with hands-on programming assignments that develop practical implementation skills. By the conclusion of the course, students will have gained the knowledge and skills to navigate and contribute to the rapidly evolving field of NLP.
Chapters
- Getting Started
- Text Processing
- Language Models
- Large Language Models
- Vector Space Models
- Distributional Semantics