Syllabus for Machine Learning in Natural Language Processing

Maskininlärning inom språkteknologi

  • 7.5 credits
  • Course code: 5LN708
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Language Technology A1F

    Main field(s) of study and in-depth level

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle
    G1N: has only upper-secondary level entry requirements
    G1F: has less than 60 credits in first-cycle course/s as entry requirements
    G1E: contains specially designed degree project for Higher Education Diploma
    G2F: has at least 60 credits in first-cycle course/s as entry requirements
    G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    GXX: in-depth level of the course cannot be classified.

    Second cycle
    A1N: has only first-cycle course/s as entry requirements
    A1F: has second-cycle course/s as entry requirements
    A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    AXX: in-depth level of the course cannot be classified.

  • Grading system: Fail (U), Pass (G), Pass with distinction (VG)
  • Established: 2011-11-25
  • Established by:
  • Revised: 2019-05-13
  • Revised by: The Department Board
  • Applies from: week 20, 2019
  • 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.
  • Responsible department: Department of Linguistics and Philology

Learning outcomes

In order to pass the course, a student must be able to

  1. apply basic principles of machine learning to natural language data;
  2. apply probability theory and statistic inference on linguistic data;
  3. use standard software packages for machine learning;
  4. implement linear models for classification;
  5. 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.

Assessment

The course is examined by means of shorter lab assignments completed in class and four larger assignments.

Reading list

The reading list is missing. For further information, please contact the responsible department.