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Before choosing courseDD1418 Language Engineering with Introduction to Machine Learning 6.0 creditsAdministrate About course

This first-cycle course in language technology treats different methods for analysis, generation, and filtering of human language especially text. Rule-based and statistical methods are used and studied in applications such as information retrieval, spelling- and grammar checking, and machine translation. It will also give an introduction to machine learning and examples of how machine learning can be used in language technology.

The course covers theory, methods, and application areas within language technology.

Choose semester and course offering

Choose semester and course offering to see information from the correct course syllabus and course offering.

* Retrieved from Course syllabus DD1418 (Autumn 2019–)

Content and learning outcomes

Course contents


The historical development and bases of language engineering, morphology, syntax, semantics, vector space models, evaluation methods, machine learning, information theory and Markov models.


Morphological analysis, generation and language statistics and corpus processing, parsing, generation, part-of-speech tagging, named entity recognition, probabilistic parsing and statistical lexical semantics.

Application areas:

Spelling and grammar checking, information retrieval, word prediction for smart text entry, text clustering and text categorization, computer-aided language learning, dialogue systems, speech technology and machine translation.

Intended learning outcomes

Having passed the course, the student should be able to:

  1. explain and use basic concepts in linguistics, language engineering and machine learning
  2. apply language engineering concepts, methods and tools to build language engineering systems as well as be able to explain the structure of such systems
  3. implement standard methods in language engineering
  4. design and carry out simple evaluations of a language engineering system as well as interpret the results,
  5. independently be able to solve a well delimited practical language engineering problem

in order to be able to

  • work with a bachelor's degree project with a focus on language engineering or machine learning,
  • be an important link between systems designers, programmers, and interaction designers in industry as well as in research projects.

Course Disposition

Theoretical lectures and applied lectures interleaved with practical laboratory sessions. A final project work that is presented orally and in writing.

Literature and preparations

Specific prerequisites

Completed course in computer science equivalent to DD1320/DD1321 Applied computer science, DD1327 Fundamentals of computer science or DD1338 Algorithms and Data Structures.

Completed course in probability theory and statistics equivalent to SF1918.

Recommended prerequisites

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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


  • LAB1 - Laboratory Assignments, 1,5 hp, betygsskala: P, F
  • PRO1 - Project, 1,5 hp, betygsskala: A, B, C, D, E, FX, F
  • TEN1 - Exam, 3,0 hp, betygsskala: 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.

It is the examiner who, in agreement with KTH´s coordinator for disabilities, will decide on possible adapted examination for students with a proven permanent disability. The examiner may permit another examination format at the re-examination of individual students.

Other requirements for final grade

Passed laboratory course, project assignment and exam.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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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 web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web DD1418

Offered by

EECS/Intelligent Systems

Main field of study


Education cycle

First cycle

Add-on studies

For example DD2476 Search Engines and Information Retrieval Systems, DT2112 Speech Technology.


Johan Boye

Supplementary information

The course is overlapping DD2418. DD1418 is a first-cycle course and has a less advanced project than DD2418. DD1418 cannot be combined with DD2418.

In this course, the EECS code of honor applies, see: