Skip to main content
Till KTH:s startsida

ID1214 Artificial Intelligence and Applied Methods 7.5 credits

Administer About course

The course gives an overview of Artifical Intelligence and Applied Methods.

The focus is on several different areas of Artifical Intelligence with AI-problems, and Methods and includes areas such as: Intelligent /Knowledge-based systems, Agent / multi-agent systems, Natural language processing and strategies.

Information per course offering

Termin

Information for Autumn 2026 Start 26 Oct 2026 programme students

Course location

KTH Campus

Duration
26 Oct 2026 - 11 Jan 2027
Periods

Autumn 2026: P2 (7.5 hp)

Pace of study

50%

Application code

10751

Form of study

Normal Daytime

Language of instruction

English

Course memo
Course memo is not published
Number of places

Min: 1

Target group
Open to all programmes as long as it can be included in your programme.
Planned modular schedule
[object Object]
Schedule
Schedule is not published

Contact

Examiner
No information inserted
Course coordinator
No information inserted
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 ID1214 (Autumn 2026–)
Headings with content from the Course syllabus ID1214 (Autumn 2026–) are denoted with an asterisk ( )

Content and learning outcomes

Course contents

The following fields are treated within the scope of the course:

  • Fundamental AI problems and solutions including search algorithms and planning, knowledge representation forms and knowledge including reasoning strategies, decision support and heuristics.
  • Intelligent agents and multi-agent systems
  • Automatic analysis and generation of natural language.
  • Machine learning and neural networks.

Focus is on artificial intelligence for knowledge-based systems, agent system and strategies.

Intended learning outcomes

After passing the course, the students should be able to:

  • give an account of artificial intelligence and its application areas
  • know and account for artificial intelligence methods and technologies
  • formulate and carry out a well delimited and qualified assignment that applies artificial intelligence techniques.

Literature and preparations

Specific prerequisites

Knowledge in calculus in one variable, 5 credits, equivalent to completed course SF1696/IX1303/SF1625/SF1685/HF1006.

Knowledge in linear algebra, 5 credits, equivalent to completed course SF1697/IX1304/SF1624/SF1684/HF1006.

Knowledge in discrete mathematics, 7.5 credits, equivalent to completed course IX1500/SF1698/SF1610/CM1000. Active participation in IX1500 or SF1698 during study period 1 of the same academic year is considered equivalent to completed course. Registered students are expected and considered to be actively participating.

Knowledge in probability theory and statistics, 6 credits, equivalent to completed course IX1501/SF1900/SF1912/HF1012. Active participation in IX1501, SF1900 or SF1912 during study period 1 of the same academic year is considered equivalent to completed course. Registered students are expected and considered to be actively participating.

Knowledge and skills in programming, 6 credits, corresponding to completed course ID1022/ID1018/HI1024.

Knowledge in algorithms and data structures, 6 credits, corresponding to completed course ID1021/HI1029.

Additional skills in independent software development, 12 credits, from completed courses in computer science, computer engineering or numerical methods with laboratory elements that are not performed in a larger group of two people. These courses are in addition to the courses mentioned above.

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

  • INL1 - Written assignment, 4.0 credits, grading scale: P, F
  • TEN1 - Examination, 3.5 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.

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

Written examination. Written assignment that is reported in groups.

Examiner

Profile picture Anne Håkansson

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/Computing and Learning Systems

Main field of study

Technology

Education cycle

First cycle

Supplementary information

In this course, the EECS code of honor applies, see: http://www.kth.se/en/eecs/utbildning/hederskodex.