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DD2380 Artificial Intelligence 6.0 credits

The course gives a broad overview of the problems and methods studied in the field of artificial intelligence.

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

Headings with content from the Course syllabus DD2380 (Autumn 2021–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The following fields are treated within the scope of the course: problem-solving with search algorithms, heuristics, knowledge representations (logic), planning,representation of uncertainty and inference (Bayesian networks, HMM), decision theory and utility theory, diction (NLP).

Intended learning outcomes

After passing the course, the student should be able to

  1. apply different principles of Artificial Intelligence (AI)
  2. choose appropriate tools and implement efficient solutions to problems in AI
  3. integrate tools to design computer programs that show different properties that are expected by an intelligent system
  4. present, analyse, and entitle an own solution to an AI problem
  5. reflect on and discuss current social and ethical aspects of AI

in order to be able to

  • draw use of methods of artificial intelligence  in analysis, design and implementation of computer programs
  • contribute to design of an intelligent system in both academic and industrial applications.

Course disposition

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Literature and preparations

Specific prerequisites

  • Knowledge in linear algebra, 7,5 credits, corresponding to completed course SF1624/SF1672.
  • Knowledge in calculus in one variable, 7,5 credits, corresponding to completed course SF1625/SF1673.
  • Knowledge and skills in programming, 6 credits, corresponding to completed course DD1337/DD1310/DD1311/DD1312/DD1314/DD1315/DD1316/DD1318/DD1321/DD1331/DD100N/ID1018.
  • Knowledge in algorithms and data structures, 6 credits, corresponding to completed course DD1338/DD1320/DD1321/DD1325/DD1327/DD2325/ID1020/ID1021.

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

Students who are planning to take the course autumn semester 2020 are given exemption from the specific prerequisite regarding regarding mathematics corresponding to SF1546 Numerical methods basic course. 


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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 - Labs, 4.0 credits, grading scale: P, F
  • RAP1 - Report, 0.5 credits, grading scale: P, F
  • TEN2 - Written exam, 1.5 credits, grading scale: P, 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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Profile picture Jana Tumová

Profile picture Iolanda Dos Santos Carvalho Leite

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 DD2380

Offered by

EECS/Intelligent Systems

Main field of study

Computer Science and Engineering

Education cycle

Second cycle

Add-on studies

DD2431 Machine Learning
DD2434 Machine Learning, advanced course
DD2424 Deep learning in data science
DD2432 Artificial Neural Networks and Other Learning Systems 
DD2423 Image Analysis and Computer Vision
DD2425 Robotics and Autonomous Systems
DD2429 Computational Photography
EL2320 Applied Estimation


Iolanda Dos Santos Carvalho Leite (

Transitional regulations

The earlier course moment TEN1 is replaced by TEN2 and RAP1.

Supplementary information

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