General Information
| Course Code | L_PAMATLW006 |
|---|---|
| Credits | 6 EC |
| Period | P4 |
| Course Level | 400 |
| Language of Tuition | English |
| Faculty | Fac. of Social Sciences and Humanities |
| Course Coordinator | prof. dr. A.S. Fokkens |
| Examiner | prof. dr. A.S. Fokkens |
| Teaching Staff |
prof. dr. A.S. Fokkens |
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Part of programme(s)
Master
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Practical Information
You need to register for this course yourself. The (de)registration deadlines can be found on VU.nl
| Teaching Methods | Seminar |
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Target audiences
This course is also available as:
Course Objective
This course prepares students for their language AI internship or research thesis and covers various topics that are useful for when entering the workfloor. They learn to develop a language and AI research hypothesis starting from a real-world problem and acquire fundamental research techniques for conducting a literature study of related work and preparing a data plan. The course furthermore covers the basics of domain adaptation and ethics which are a fundamental component of applying NLP solutions in real world scenarios.
Course Content
Students doing an internship need to be able to combine the practical needs and techniques they encounter during their internship with academic standards. Similarly, students doing a research thesis need to deal with the challenges of more complex questions and tasks compared to course work. Working with these real world scenarios (compared to targeted course work) requires a good understanding of how to deal with data from different domains and what ethical concerns may arise when using NLP in various situations. In this course, scientific methods acquired during the Master's programme will be intensified. Language and AI projects require a combination of data collection and analysis skills with the ability to work with state-of-the-art NLP methods and models. Students prepare for their thesis by setting up their research question and the outline for their thesis together with the their supervisor. In parallel, students study the basics of domain adaptation and ethics.
Knowledge and ideas about ethics and domain adaptation are shared in discussions about the individual projects.
Additional Information Teaching Methods
The course consists of various interactive sessions that cover topics around domain adaptation, ethics as well as specific skills for carrying out the final research projects. Active participation is necessary and attendance is therefore mandatory.
Method of Assessment
The course is pass/non-pass. Since the course involves active discussion and hands-on work, attendance is obligatory. In some cases, supplementary assignments can compensate for missed classes. In order to pass the following components must meet minimal passing requirements (details will be shared with the course):
- A thesis preparation component (consisting of a thesis plan, first draft of a literature review, a data statement).
- A portfolio on ethics and domain adaptations, followed by an oral exam.
Entry Requirements
Students must have passed 18 EC of the master’s programme and must have participated in Machine learning for NLP.
Additional Information Target Audience
Students in the Master's programme Linguistics (specialisation: Language and AI). Students from other programs are welcome if (1) they qualify to start their thesis according to regulations of their program (2) their thesis addresses an NLP topic and (3) their supervisor agrees to grade the thesis preparation component.