DD2380 Artificial Intelligence 6.0 credits

Artificiell intelligens

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

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Course information

Content and learning outcomes

Course contents *

The following areas will be treated in the course: problem solving with search algorithms, heuristics and games, knowledge representation (logic), planning, representing uncertain knowledge and reasoning (Bayesian networks, HMM), decision and utility theory, natural language processing. 

Intended learning outcomes *

After completing this course the student should be able to

  1. recall and apply basic concepts in artificial intelligence
  2. solve problems from the AI domain
  3. demonstrate an insight into the risks of AI and its role in society
  4. present work

so that the student can 

  • make use of methods from artificial intelligence in the analysis, design and implementation of computer programs in academic as well as industrial applications

  • in an appropriate way present results and solutions.

Course Disposition

A series of lectures presents course material. Given the breadth of the course each lecture will not be able to go deep into a topic but rather focus on introducing the material. A couple of tutorials are added to this to provide a bit more in-depth coverage of some topics. The examination in the course consists of quizzes that test the basic concepts in the covered topics in the course, two lab assignments with programing in Java or C++ that go deeper into two areas and test the ability to solve problems in the AI domain and an optional project with research connection for higher grades. A criteria based grading system is used.

Literature and preparations

Specific prerequisites *

Single course students: 90 university credits including 45 university credits in Mathematics and/or Information Technology and the courses SF1604 Linear algebra, SF1625 Calculus in one variable, SF1626 Calculus in several variables, SF1901 Probability theory and statistics, DD1337 Programming and DD1338 Algorithms and Data Structures or equivalent. 

Recommended prerequisites

Programming skills in Java or C++ and skills in computer science corresponding to DD1337 Programming and DD1338

Algorithms and Data Structures

Furthermore SF1546 Numerical Methods Basic Course and SF1901 Probability Theory and Statistics or eqivalent.


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Artificial Intelligence: A Modern Approach by Stuart J. Russell and Peter Norvig

Examination and completion

Grading scale *

A, B, C, D, E, FX, F

Examination *

  • LAB1 - Labs, 4.0 credits, Grading scale: P, F
  • TEN1 - Exam, 2.0 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.

In this course all the regulations of the code of honor at the School of Computer science and Communication apply, see: http://www.kth.se/csc/student/hederskodex/1.17237?l=en_UK.

Other requirements for final grade *

Pass the course components (LAB1; 4 university credits) and (TEN1; 2 university credits).

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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Jana Tumová

Iolanda Dos Santos Carvalho Leite

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


Jana Tumova, tumova@kth.se

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.

Supplementary information

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