URL study guide
https://studiegids.vu.nl/en/courses/2024-2025/XM_0121Course Objective
Natural language processing (NLP) is a highly dynamic research field that aims at analyzing natural language using computational models and methods. This course provides an introduction to the principles of the field and has two main objectives: 1) Students learn the fundamentals for developing a system for a common NLP task.- They can analyze and interpret the linguistic information provided by common pre-processing pipelines and are familiar with the core NLP terminology. (Knowledge & Understanding)
- They can describe a common NLP classification setup and evaluate the results. (Making Judgements)
- They can explain the functionality of neural networks and their use in NLP. (Communication)
- They can explain the role of pre-trained language models in NLP and apply them to a task. (Applying knowledge and understanding) 2) Students learn to systematically analyze and interpret NLP models for a specific task.
- They can describe the linguistic challenges associated with the task. (Making judgments)
- They can describe how the task is approached by a state-of-the-art model. (Knowledge and understanding)
- They are able to understand, evaluate, and interpret the output of a model. (Applying knowledge and understanding)
- They can compare the output of different models and analyze their strengths and weaknesses. (Lifelong learning skills)
Course Content
Natural Language Processing operates on the interface between linguistics and computer science. In order to get computers to deal well with natural language, it is important to understand both how language works and how computational methods work. Computational linguists work on this interface and have developed methods and technologies for language analysis. This course covers technologies and computational models for core domains of natural language processing. Students are trained to find, process, and understand the latest developments in this rapidly advancing field. The course includes practical components that require students to work with Python code to explore NLP models. They learn to apply them to new data and reflect on the effect of modeling decisions on the performance.Teaching Methods
The course consists of two sessions per week. We generally alternate between more theoretical lectures and interactive practical classes in smaller groups.Method of Assessment
The course is evaluated by practical assignments (50%) and an exam(50%). Both components need to be graded at least with a 5.5. Retakes ofpractical assignments to improve the grade are not allowed. Practical assignments might be organized as a mix of group projects and individual contributions.Literature
This information will be specified on Canvas.Target Audience
Master Artificial Intelligence, also suitable for Master Business Analytics and other Master students in Computer ScienceRecommended background knowledge
Students must have acquired programming skills in Python. For students with no background knowledge of machine learning, additional reading might be required as the fundamental concepts will only be briefly introduced in the course.Language of Tuition
- English
Study type
- Master