https://taoyds.github.io/courses/comp3361
Natural language processing (NLP) is the study of human language from a computational perspective. Over the past 20 years, the field of NLP has evolved significantly, primarily driven by advancements in statistical machine learning and deep learning. A notable recent breakthrough is the development of “pre-trained” language models, such as ChatGPT, which have substantially enhanced capabilities across various applications. This course is an introductory undergraduate-level course on natural language processing. In this class, we will cover recent developments on core techniques and modern advances in NLP, especially in the era of large language models. Students will gain the necessary skills and experience to understand, design, implement, and test large language models through lectures and assignments. We will potentially also host invited speakers for talks.
- COMP3314 or COMP3340; and MATH1853
- We require students to have prior knowledge undergraduate linear algebra, probability and statistics, machine learning, or deep learning. Familiarity with Python programming is required.
We will review your work one by one to ensure that you receive the credit you deserve. Please note that we will consider the output and logic of your project for grading. Late submissions will not be graded.
You are free to discuss ideas and implementation details with other students. However, copying others' code will not help your learning and may jeopardize it. We will compare your work with other submissions and internet resources. It is easy to tell if you have completed your own work. For clarity, we encourage you to discuss with classmates, but you must independently complete your own work and not simply copy and paste. If plagiarism is found, serious consequences may be faced according to departmental and university policies.