Natural Language Processing

This course provides an introduction to natural language processing (NLP), covering both theoretical concepts and practical applications of NLP techniques. Through this course, you will gain a foundational understanding of the fundamental concepts related to NLP and become familiar with various NLP techniques. Additionally, you will have the opportunity to gain hands-on experience implementing NLP techniques using programming tools in Python. Furthermore, this course will expose you to some of the latest NLP models currently used in production, including BERT, transformers, and large language models. You will also explore real-world applications of NLP, such as chatbots, to gain a better understanding of the practical use of NLP techniques.

The assessment of this course will be based on a final exam and a group project. The exam (3 hours) will take place in week 1.10, and the final group project will be due in week 1.9. The group project will be done in groups of 3-5 students.

To pass, you must obtain a grade greater or equal to 5.0 for both the final exam and the final project and at least a 5.8 for the final grade after rounding. For the final exam, there is a resit opportunity. The resit grade replaces the original grade only if the resit grade is greater than the original grade.

The final project consists of a practical group project (coding) and a short written report. Each group will have a go/no-go moment where the students will receive feedback on their project idea. For the written report, there will be a repair possibility. Groups that receive a grade below 5.8 can resubmit an improved version of their report one week after the initial deadline.

The weighting of the final grade is:

Exam: 60%

Final project: 40%

Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 29, sub 4)