Machine Learning in Natural Language Processing
Syllabus, Master's level, 5LN708
- Code
- 5LN708
- Education cycle
- Second cycle
- Main field(s) of study and in-depth level
- Language Technology A1F
- Grading system
- Fail (U), Pass (G), Pass with distinction (VG)
- Finalised by
- The Department Board, 18 August 2014
- Responsible department
- Department of Linguistics and Philology
Entry requirements
Bachelor's degree and at least 60 credits in language technology/computational linguistics; or at least 60 credits in computer science; or at least 60 credits in a linguistic subject along with 15 credits in programming and 7.5 credits logic/discrete mathematics.
Learning outcomes
In order to pass the course, a student must be able to
- apply basic principles of machine learning to natural language data;
- apply probability theory and statistic inference on linguistic data;
- use standard software packages for machine learning;
- implement linear models for classification;
- apply clustering techniques on linguistic data.
with a certain degree of independent creativity, clearly stating and critically discussing methodological assumptions, applying state-of-the-art methods for evaluation, and presenting the result in a professionally adequate manner.
Instruction
The teaching is given as lectures and laboratory sessions under supervision.
Assessment
The course is examined by means of shorter lab assignments completed in class and three larger assignments.