Syllabus for Machine Learning in Language Technology

Maskininlärning i språkteknologi

  • 7.5 credits
  • Course code: 5LN454
  • Education cycle: First cycle
  • Main field(s) of study and in-depth level: Language Technology G1F

    Explanation of codes

    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: 2012-03-16
  • Established by: The Department Board
  • Revised: 2016-09-02
  • Revised by: The Department Board
  • Applies from: Autumn 2016
  • Entry requirements:

    Mathematics for Language Technologists or equivalent

  • 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. evaluate the performance of machine learning schemes;
  3. use standard off-the-shelf software for machine learning;
  4. 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.

Syllabus Revisions

Reading list

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

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