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).
DD2380 Artificial Intelligence 6.0 credits
The course gives a broad overview of the problems and methods studied in the field of artificial intelligence.
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.
Course syllabus as PDF
Please note: all information from the Course syllabus is available on this page in an accessible format.
Course syllabus DD2380 (Autumn 2026–)Headings with content from the Course syllabus DD2380 (Autumn 2026–) are denoted with an asterisk ( )
Content and learning outcomes
Course contents
Intended learning outcomes
After passing the course, the student shall be able to
- apply different principles of Artificial Intelligence (AI)
- choose appropriate tools and implement efficient solutions to problems in AI
- integrate tools to design computer programs that show different properties that are expected by an intelligent system
- present, analyse, and entitle an own solution to an AI problem
- 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.
Literature and preparations
Specific prerequisites
Knowledge in linear algebra, 7.5 credits, equivalent to completed course SF1624/SF1672/SF1684.
Knowledge in one variable calculus, 7.5 credits, equivalent to completed course SF1625/SF1673/SF1685.
Knowledge in probability theory and statistics, 6 credits, equivalent to completed course SF1910-SF1925/SF1935 or completed exam module TEN1 within SF1910/SF1925/SF1935.
Knowledge and skills in programming, 6 credits, equivalent to completed course DD1337/DD1310-DD1319/DD1321/DD1331/DD1333/DD100N/ID1018/ID1022.
Knowledge of algorithms, data structures and basic software development techniques, 6 credits, equivalent to completed course DD1338/DD1320-DD1328/DD2325/ID1020/ID1021 or completed exam elements KONT and LABD in DD1326.
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
Grading scale
A, B, C, D, E, FX, F
Examination
- RAP1 - Report, 0.5 credits, grading scale: P, F
- LAB2 - Laboratory work, 4.0 credits, grading scale: A, B, C, D, E, FX, F
- TENQ - Quiz, 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.
If the course is discontinued, students may request to be examined during the following two academic years.
Examiner
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
Computer Science and Engineering
Education cycle
Second cycle
Transitional regulations
The earlier examination LAB1 has been replaced by LAB2 and TEN2 has been replaced by TENQ.
Supplementary information
In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex