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DD2417 Language Engineering 7.5 credits

Administer About course

In the Language Engineering course you will learn grammatical, statistical, and neural methods for analysis and generation of (written) human languages.

Information per course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.

Termin

Information for Spring 2026 Start 16 Mar 2026 programme students

Course location

KTH Campus

Duration
16 Mar 2026 - 1 Jun 2026
Periods
P4 (7.5 hp)
Pace of study

50%

Application code

60246

Form of study

Normal Daytime

Language of instruction

English

Course memo
Course memo is not published
Number of places

Places are not limited

Target group

Open for all programmes as long as the course can be included in your program.

Planned modular schedule
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Schedule
Schedule is not published

Contact

Examiner
No information inserted
Course coordinator
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Teachers
No information inserted

Course syllabus as PDF

Please note: all information from the Course syllabus is available on this page in an accessible format.

Course syllabus DD2417 (Autumn 2025–)
Headings with content from the Course syllabus DD2417 (Autumn 2025–) are denoted with an asterisk ( )

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,

in order to be able to

  • work for language technology companies
  • carry out a master's degree project in computer science with a specialisation in language engineering
  • be an important link between systems designers, programmers, and interaction designers in industry as well as in research projects.

Literature and preparations

Specific prerequisites

Knowledge and skills in programming, 6 credits, equivalent to completed course DD1310/DD1311/DD1312/DD1314/DD1315/DD1316/DD1318/DD1331/DD1337/DD100N/ID1018.

Knowledge in algorithms and data structures, 6 credits, equivalent to completed course DD1320/DD1321/DD1325/DD1327/DD1338/ID1020/ID1021.

Knowledge in probability theory, equivalent to course SF1912/SF1914-SF1924.

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.

Literature

You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

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

Examination

  • LAB1 - Computer assignments, 6.0 credits, grading scale: A, B, C, D, E, FX, F
  • PRO1 - Project assignment, 1.5 credits, grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Examiner

Profile picture Johan Boye

Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

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, see:
http://www.kth.se/en/eecs/utbildning/hederskodex

DD2417 is overlapping DD2418 and partly DD1418 and therefore cannot be combined with these courses.