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DD1418 Language Engineering with Introduction to Machine Learning 6.0 credits

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.

About course offering

For course offering

Autumn 2024 Start 28 Oct 2024 programme students

Target group

Open for all programmes provided that the prerequisites are met and that the course can be included in your programme.

Part of programme

Degree Programme in Industrial Engineering and Management, åk 3, DTOI, Mandatory

Degree Programme in Industrial Engineering and Management, åk 3, TMAI, Mandatory

Master of Science in Engineering and in Education, åk 4, TEDA, Conditionally Elective

Master of Science in Engineering and in Education, åk 5, TEDA, Conditionally Elective


P2 (6.0 hp)


28 Oct 2024
13 Jan 2025

Pace of study


Form of study

Normal Daytime

Language of instruction


Course location

KTH Campus

Number of places

Places are not limited

Planned modular schedule


For course offering

Autumn 2024 Start 28 Oct 2024 programme students

Application code



For course offering

Autumn 2024 Start 28 Oct 2024 programme students


Johan Boye


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Course coordinator

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Headings with content from the Course syllabus DD1418 (Autumn 2021–) are denoted with an asterisk ( )

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

After passing the course, the student shall 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.

Literature and preparations

Specific prerequisites

  • Knowledge and skills in programming, 6 higher education credits, equivalent to completed course DD1310/DD1311/DD1312/DD1314/DD1315/DD1316/DD1318/DD1331/DD1337/DD100N/ID1018.
  • Knowledge in basic computer science, 6 higher education credits, equivalent to completed course DD1320/DD1321/DD1325/DD1327/DD1338/ID1020/ID1021.
  • Knowledge in probability theory, 6 higher education credits, equivalent to completed course SF1912/SF1914-SF1924.

Active participation in a course offering where the final examination is not yet reported in LADOK is considered equivalent to completion of the course. Registering for a course is counted as active participation. The term 'final examination' encompasses both the regular examination and the first re-examination.

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 credits, grading scale: P, F
  • PRO1 - Project, 1.5 credits, grading scale: A, B, C, D, E, FX, F
  • TEN1 - Exam, 3.0 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.

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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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

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 DD2417. DD1418 is a first-cycle course and has a less advanced project than DD2417. DD1418 cannot be combined with DD2417 or DD2418.

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