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Blekinge Institute of Technology
Department of Computer Science

Revision: 2
Reg.no:


Course syllabus

Applied artificiell intelligens

Applied artificiell intelligens

6 credits (6 högskolepoäng)

Course code: DV2659
Main field of study: Computer Science, Technology
Disciplinary domain: Technology
Education level: Second-cycle
Specialization: A1N - Second cycle, has only first-cycle course/s as entry requirements

Language of instruction: English
Applies from: 2025-04-15
Approved: 2025-04-08

1. Descision

This course is established by Dean 2024-12-13. The course syllabus is approved by Head of Department of Computer Science 2025-04-08 and applies from 2025-04-15.

2. Entry requirements

Admission to the course requires 5 completed credits in programming, 5 completed credits in data structures and algorithms as well as a taken course in mathematical statistics.

3. Objective and content

3.1 Objective

Artificial intelligence (AI) in various forms is present in an increasing proportion of the computerized systems we use – intelligent agents, decision support systems, optimization techniques, machine learning and hybrid systems. The course also addresses digital ethics in relation to these technologies and systems. The course aims to introduce the field of artificial intelligence and some of the problems within the various research areas where AI has proven powerful.

3.2 Content

The course provides a historical overview of AI, with an emphasis on key milestones from an application perspective. Topics covered include:

• introduction to AI,
• knowledge representation, 
• expert systems, 
• graphs, search and heuristics,
• agent systems,
• digital ethics, 
• data mining and knowledge discovery,
• machine learning, including various learning paradigms such as deep learning, and
• modern applications of AI, such as its use in machine vision and natural language processing.

4. Learning outcomes

The following learning outcomes are examined in the course:

4.1. Knowledge and understanding

On completing the course, the student should be able to:

  • Explain AI, its applications and important subfields

4.2. Competence and skills

On completing the course, the student should be able to:

  • Communicate in written form the strengths and weaknesses of different AI methods
  • Suggest appropriate AI method(s) for a given problem
  • Design, develop and implement AI solutions to relevant problems using a programming language
  • Evaluate the performance of basic AI applications

4.3. Judgement and approach

On completing the course, the student should be able to:

  • Critically examine and reason about the potential and limits of AI methods
  • Explain ethical and sustainability-related issues in the field of AI

5. Learning activities

The course is given in the form of lectures, exercises and laboratory sessions. The lectures are given on campus. Exercises and laboratory sessions are carried out in small groups, where students practice the abilities, skills and attitudes needed to meet the course objectives.

6. Assessment and grading

Modes of examinations of the course

Code Module Credit Grade
2510 Written assignment 1 1 credits GU
2520 Written assignment 2 2 credits GU
2530 On-campus Examination[1] 3 credits AF

[1] Determines the final grade for the course, which will only be issued when all components have been approved.

The course will be graded A Excellent, B Very good, C Good, D Satisfactory, E Sufficient, FX Failed result, a little more work required, F Fail.

The examiner may carry out oral follow-up of written examinations.

The information before the start of the course states the assessment criteria and make explicit in which modes of examination that the learning outcomes are assessed.

An examiner can, after consulting the Disability Advisor at BTH, decide on a customized examination form for a student with a long-term disability to be provided with an examination equivalent to one given to a student who is not disabled.

7. Course evaluation

The course evaluation should be carried out in line with BTH:s course evaluation template and process.

8. Restrictions regarding degree

The course can form part of a degree but not together with another course the content of which completely or partly corresponds with the contents of this course.

9. Course literature and other materials of instruction

Book-1:
Artificial Intelligence: A Guide to Intelligent Systems (4th Edition)
Författare: Michael Negnevitsky
Förlag: Addison-Wesley
Utgiven: 2024
ISBN: 9781292730851

Book-2:
Artificial Intelligence – A modern approach, 4th ed Författare: Stuart Russell & Peter Norvig
Förlag: Prentice Hall
Utgiven: 2020, Antal sidor: 1136
ISBN-10: 0-13-461099-7

10. Additional information

This course replaces DV2619