Principles of Artificial Intelligence
2026/2027
Content, progress and pedagogy of the module
Learning objectives
Knowledge
A student completing the module should gain knowledge of:
- Mathematical and computational foundations for AI and ML.
- Fundamentals of data preprocessing and representation techniques, including e.g., encoding, scaling, and balancing, for various data types.
- Fundamental and state-of-the-art AI architectures and methods, e.g., deep neural networks.
- Fundamentals of AI/ML model tuning and validation.
Skills
A student completing the module should be able to:
- Conceptualize, create, and train supervised and unsupervised AI architectures and methods.
- Design preprocessing pipelines for various data types, including tabular, image, time series, and text data, creating end-to-end data preparation workflows.
- Utilize modern methodologies and software tools to construct and implement AI/ML models.
- Apply hyperparameter tuning to improve AI/ML model performance.
- Accelerate the training and evaluation of AI methods using high performance computing environments.
Competences
A student completing the module should be able to:
- Identify and address challenges relevant to AI/ML development within target systems.
- Evaluate available AI/ML techniques and select suitable ones to solve defined problems.
- Analyse and solve problems related to data quality, representation, and the performance of AI/ML models.
- Explain AI/ML model’s concepts and their results to both technical and non-technical audiences.
Type of instruction
The type of instruction is organized in accordance with the general instruction methods of the programme, cf. § 17.
Extent and expected workload
Since it is a 5 ECTS course module the expected workload is 150 hours for the student.
Exam
Exams
| Name of exam | Principles of Artificial Intelligence |
| Type of exam | Written or oral exam |
| ECTS | 5 |
| Permitted aids | Information about allowed helping aids for the examination will be published in Moodle and/or in Digital Exam. |
| Assessment | 7-point grading scale |
| Type of grading | Internal examination |
| Criteria of assessment | The criteria of assessment are stated in the Examination Policies and Procedures |
Facts about the module
| Danish title | Grundprincipper for kunstig intelligens |
| Module code | M-OSM-K1-5 |
| Module type | Course |
| Duration | 1 semester |
| Semester | Autumn
|
| ECTS | 5 |
| Language of instruction | English |
| Empty-place Scheme | Yes |
| Location of the lecture | Campus Aalborg |
| Responsible for the module |
Organisation
| Education owner | Master of Science (MSc) in Engineering (Management Engineering) |
| Study Board | Study Board of Production |
| Department | Department of Materials and Production |
| Faculty | The Faculty of Engineering and Science |
