Machine Learning in Language Technology
7.5 credits
Syllabus, Bachelor's level, 5LN454
A revised version of the syllabus is available.
- Code
- 5LN454
- Education cycle
- First cycle
- Main field(s) of study and in-depth level
- Language Technology G1F
- Grading system
- Pass with distinction (VG), Pass (G), Fail (U)
- Finalised by
- The Department Board, 2 September 2016
- Responsible department
- Department of Linguistics and Philology
Entry requirements
Mathematics for Language Technologists or equivalent
Learning outcomes
In order to pass the course, a student must be able to:
- apply basic principles of machine learning to natural language data;
- evaluate the performance of machine learning schemes;
- use standard off-the-shelf software for machine learning;
- apply supervised and unsupervised models for classification.
Assessment
The course is examined by means of shorter lab assignments completed in class and three larger assignments.