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NAB3044 Maritime Decision Support Systems, Artificial Intelligence, Machine Learning and Digital Twins

Course description for academic year 2024/2025

Contents and structure

The course lays the groundwork for understanding and analysing information and information systems. It also covers the technical and organizational aspects of decision support systems (DSS), as well as the application of artificial intelligence (AI), machine learning (ML), and digital twins (DT) in maritime settings. The course provides an overview of recent digitalization technologies, date-driven methods for creating and simplifying prediction/classification models, and DT fundamentals. While the course is user-oriented and emphasizes the conceptual foundations of DSS, AI, ML, and DT, it also includes demonstrations, laboratory lessons, and case examples.

NAB3044 is an elective course - reservation regarding the 2024/2025 academic year, among other things considering if there are enough students enrolled in the course.

Learning Outcome

The student

  • has a broad understanding of what influences decision making at the individual, group, and organizational levels
  • has knowledge about methods and techniques for creating purposeful DSS
  • understands foundational concepts and trends in AI and ML, as well as potential real-world implications
  • determines when machine learning is feasible and can be meaningfully applied to specific challenges
  • leverages the power of data and evaluate common machine learning methods to improve predictive performance and refine decision-making strategies
  • has introductory knowledge about digital twin concepts, entities, models and services.
  • is familiar with methods in the field of digital twins.

The student

  • can perform basic applications of DSS, ML, and DT
  • has the ability to apply knowledge and communicate results.

The student

  • can communicate independent work as well as DSS, AI, ML, and DT terminology
  • is aware of new trends and innovations in the areas of DSS, AI, ML, and DT.

Entry requirements


Recommended previous knowledge

General abilities to use digital systems and tools.

Teaching methods

Lectures, laboratory lessons, and seminars.

Compulsory learning activities

One poster/presentation.


Portfolio with two portfolio elements, 100%

Grading scale A-F

Examination support material

Open book - all allowed

More about examination support material