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, 6 September 2019
- Responsible department
- Department of Linguistics and Philology
Entry requirements
A Bachelor's degree and (1) 60 credits in language technology/computational linguistics, or (2) 60 credits in computer science, or (3) 60 credits in a language subject, 7.5 credits in computer programming and 7.5 credits in logic/discrete mathematics. Knowledge of English equivalent to what is required for entry to Swedish first-cycle courses and study programmes.
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;
- design simple neural nets using some standard library.
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 four larger assignments.
If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator.