Machine Learning Data Analysis
2025/2026
Content, progress and pedagogy of the module
Disclaimer.
This is an English translation of the module. In case of
discrepancy between the translation and the Danish version, the
Danish version of the module is valid.
PURPOSE
Students will learn how to use modern machine learning methods for
data analysis. Potential legal and ethical aspects of such analyses
must be considered.
JUSTIFICATION
Machine learning provides powerful tools to construct abstract data
models and use these models to make predictions about unseen data.
The ability to competently use these tools is a central skill in
data science. In this project module, students focus on the
application of techniques from machine learning and their relation
to artificial intelligence. When machine learning methods are
applied to real-world datasets, legal and ethical aspects must be
considered.
Learning objectives
Knowledge
have knowledge of a number of relevant techniques from machine learning, their potential strengths and limitations for a given data analysis problem, as well as methods for quantitative evaluation of machine learning models.
have knowledge of relevant legal and ethical aspects of applying machine learning techniques to data that may contain sensitive personal data or business data.
Skills
be able to apply relevant machine learning techniques to real-world data using appropriate software tools and programming languages.
be able to document the results of data analysis with machine learning using appropriate evaluation methods.
Competences
be able to select relevant machine learning techniques for a given data analysis problem.
be able to interpret the results of data analysis with machine learning and understand their potential strengths and weaknesses.
Type of instruction
Project work
Extent and expected workload
The student is expected to spend 30 hours per ECTS, which for this activity means 450 hours.
Exam
Exams
| Name of exam | Machine Learning Data Analysis |
| Type of exam | Oral exam based on a project |
| ECTS | 15 |
| Permitted aids | Aids are permitted during the preparation of the project, but not during the exam. Rules regarding AI are mentioned on the semester page in MOODLE |
| Assessment | 7-point grading scale |
| Type of grading | External examination |
| Criteria of assessment | The criteria of assessment are stated in the Examination Policies and Procedures |
Additional information
Contact: Study Board for Computer Science via cs-sn@cs.aau.dk or 9940 8854
Facts about the module
| Danish title | Dataanalyse via maskinlæring |
| Module code | DSNDVMLB331 |
| Module type | Project |
| Duration | 1 semester |
| Semester | Autumn
|
| ECTS | 15 |
| Language of instruction | Danish |
| Location of the lecture | Campus Aalborg |
| Responsible for the module |
Organisation
| Education owner | Bachelor of Science (BSc) in Data Science and Machine Learning |
| Study Board | Study Board of Computer Science |
| Department | Department of Computer Science |
| Faculty | The Technical Faculty of IT and Design |
