Program Overview

Course Overview

The Language Engineering course, DD2417, is a 7.5-credit course that focuses on grammatical, statistical, and neural methods for analysis and generation of written human languages.

Information per Course Offering

Termin

The course is offered in the Spring 2026 term, starting on March 16, 2026, and ending on June 1, 2026.

Course Details

• Course location: KTH Campus
• Duration: March 16, 2026 - June 1, 2026
• Periods: Spring 2026: P4 (7.5 hp)
• Pace of study: 50%
• Application code: 60246
• Form of study: Normal Daytime
• Language of instruction: English
• Number of places: Places are not limited
• Target group: Open for all programmes as long as the course can be included in your program.

Part of Programme

The course is part of the following programmes:

• Master's Programme, ICT Innovation, year 2, DASE
• Master's Programme, Computer Science, year 1, CSDA
• Master of Science in Engineering and in Education, year 5, TEDA
• Master's Programme, ICT Innovation, year 2, DASC
• Master's Programme, Computer Science, year 2, CSCS
• Master of Science in Engineering and in Education, year 4, TEDA
• Master's Programme, ICT Innovation, year 1, DASE
• Master's Programme, Computer Science, year 1, CSCS
• Master's Programme, ICT Innovation, year 1, DASC
• Master's Programme, Machine Learning, year 1

Course Syllabus

The course syllabus is available as a PDF and includes information on the course contents, intended learning outcomes, and examination.

Content and Learning Outcomes

Course Contents

• Levels for the analysis of written human language: Morphology, syntax, semantics, and pragmatics
• Grammatical, statistical, and neural methods for linguistic analysis and generation.

Intended Learning Outcomes

After passing the course, the student shall be able to:

• Explain and use concepts at the basic levels of linguistics: morphology, syntax, semantics, discourse, and pragmatics,
• Explain, implement, and use standard methods of language engineering that are based on rules, statistics, and machine learning,
• Use basic language engineering tools, corpora, and software libraries
• Design and carry out simple evaluations of some language engineering system, and interpret the results.

Literature and Preparations

Specific Prerequisites

• Knowledge and skills in programming, 6 credits, equivalent to completed course DD1337/DD1310-DD1319/DD1321/DD1331/DD1333/DD100N/ID1018/ID1022.
• Knowledge in algorithms and data structures, 6 credits, equivalent to completed course DD1338/DD1320-DD1328/DD2325/ID1020/ID1021.
• Knowledge in probability theory and statistics, equivalent to course SF1910-SF1925/SF1935 or completed TEN1 within SF1910/SF1925/SF1935.

Recommended Prerequisites

• Knowledge of formal languages corresponding to DD2481 Principles of Programming Languages, DD2372/DD2373 Automata and Languages, or DD1360/DD1361/DD1362 Programming paradigms is useful but not necessary.

Examination and Completion

Grading Scale

A, B, C, D, E, FX, F

Examination

• PRO1 - Project assignment, 1.5 credits, grading scale: A, B, C, D, E, FX, F
• LABA - Computer Laboratory Work, 3.0 credits, grading scale: P, F
• TENA - Written Exam, 3.0 credits, grading scale: A, B, C, D, E, FX, F

Further Information

Course Room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas.

Offered by

EECS/Intelligent Systems

Main Field of Study

Computer Science and Engineering

Education Cycle

Second cycle

Supplementary Information

In this course, the EECS code of honor applies. DD2417 is overlapping DD2418 and partly DD1418 and therefore cannot be combined with these courses.