Not applicable; desired background includes exposure to Python, machine learning and NLP (e.g. through the BA course Natural Language Processing (LIACS), and electives in the MA track Computational Linguistics).
In this course, students learn about the theory and practice of a variety of machine learning algorithms for natural language processing (NLP). The course starts with a 4-class block discussing the general field, mainstream algorithms (including SVM, Gradient Boosted Trees, memory-based learning) and evaluation measures, before zooming in (the next 8 classes) on deep learning-based NLP. In this course, the leading application will be Conversational AI.
This course brings master students up to speed with current machine learning-based approaches to NLP, and introduces them to the application field of Conversational AI (chatbots, conversational agents). Students engage in practice by making (non-graded) weekly assignments using readily available machine learning toolkits, and discussing their results on shared data in class. The focus of the course is on application; students are not expected to do heavy programming. Occasional programming for data conversion, slight adaptation of existing tools and analysis may be necessary though (for which adequate support will be arranged by the teacher).
The timetables are avalable through My Timetable.
Mode of instruction
Lectures and in-class discussion of practical assignments.
The assessment method addresses:
Active Participation/coöperation in class/group (scored weekly as
A group paper (describing an experiment)
A presentation of the group paper, in which individual contributions are presented by the students.
Readining consists of papers to be administered through Brightspace and (optional)
Stephan Raaijmakers, Deep learning for Natural Language Processing. Manning, 2022. Epub and (later in 2022) hardcopy.
Enrolment through My Studymap is mandatory
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