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Applied Artificial Intelligence 7.5 credits

Tillämpad artificiell intelligens
Second cycle, D7041E
Course syllabus valid: Spring 2022 Sp 3 - Present
The version indicates the term and period for which this course syllabus is valid. The most recent version of the course syllabus is shown first.

Education level
Second cycle
Grade scale
G U 3 4 5
Computer Science
Subject group (SCB)
Computer Technology

Entry requirements

Courses of at least 120 credits at first cycle including the following knowledge/courses. The student should have knowledge about basic algorithms and data structures, programming and discrete mathematics, equivalent to the courses D0012E Algorithms and Data Structures and M0009M Discrete Mathematics.

More information about English language requirements


The selection is based on 20-285 credits

Course Aim

The course covers the concepts, models and computation methods for computer programs and systems that can autonomously, learn and generalise new knowledge and feature self-awareness.

After the course the student should be able to

  • demonstrate knowledge of the disciplinary foundation and of proven experience in the design and analysis of systems built using principles of artificial intelligence
  • demonstrate in-depth knowledge of methods and theories in the field of artificial intelligence
  • demonstrate abilities to develop learning techniques and systems based on human needs as well as the society’s goals for sustainable development
  • demonstrate the ability to identify, formulate, design, and implement learning components and applications
  • demonstrate the ability to critically evaluate and compare different AI models and learning algorithms for different problem setups and quality characteristics
  • demonstrate the ability to model, predict and evaluate the events even with limited information


Topics covered include: basic methodology, paradigms for artificial intelligence, learning methods and strategies including neural networks, evolutionary methods, instance-based learning, reinforcement learning. Methods for evaluating learning outcomes. Declarative languages, knowledge models, reasoning models. Agent architectures. Fuzzy sets.  Cognitive decision making. Associative memory. Applications including robotics and automation.

Each course occasion´s language and form is stated and appear on the course page on Luleå University of Technology's website.

The education consists of lectures, laboratory work and a seminar assignment. The laboratories are presented orally and may be provided with a deadline for submission. There are no elective course elements.  Unapproved students must retake the unsuccessful examination moment next time the course is given.

If there is a decision on special educational support, in accordance with the Guideline Student's rights and obligations at Luleå University of Technology, an adapted or alternative form of examination can be provided.
Continuous examination with laboratory work, presentations of research publications and mini-projects that give a number of points.
The mini-project is presented orally and by submitting a written report. The grade in the course is based on how many points you have accumulated.

Evgeny Osipov

Literature. Valid from Spring 2017 Sp 3 (May change until 10 weeks before course start)
There is no required textbook for this course. The course will primarily use lecture material prepared by the instructor, some recommended reference books.

Course offered by
Department of Computer Science, Electrical and Space Engineering

CodeDescriptionGrade scaleCrStatusFrom periodTitle
0001Laboratory workU G#3.00MandatoryS17
0002SeminarU G#1.00MandatoryS17
0003Mini projectG U 3 4 53.50MandatoryS17

Syllabus established
by Jonny Johansson, HUL SRT 15 Jun 2016

Last revised
by Jonny Johansson, HUL SRT 17 Feb 2021