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MSc in Business Administration and Information Systems

Ar­ti­fi­cial In­tel­li­gence and Ma­chine Learn­ing

About the course

Subjects
IT Innovation

Course content

AI is poised to transform the business and technology landscape, and it has become essential for business leaders to understand the key technologies and concepts involved. This course covers several of the main AI technologies, including natural language processing and image recognition.  The primary focus is technical, and students are expected to be able to program in Python or a similar language, and to be familiar with machine learning techniques such as classification and regression. 

 

The course addresses several key aspects of the Nordic Nine -- especially under Knowledge ("analytical with data and curious about ambiguity" and "deep
business knowledge placed in a broad context").

 

Students are expected to work with large language models and other forms of

generative AI in exercises, assignments, and exams. As with any other software, it should be clearly stated how the AI models are used in the performance of a given exercise, assignment, or exam.

 

See course description in course catalogue

What you will learn

To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:

  • Demonstrate the ability to understand and implement artificial intelligence technologies such as neural networks and deep learning, primarily as applied to natural language processing and image processing.
  • Understand basic principles of machine learning, including assessment of results using a variety of metrics, model tuning, and prompt engineering and analysis of large language models.
  • Demonstrate a practical ability to apply these machine learning techniques to relevant business cases.
  • Reflect on the societal and business impact of AI technologies.

Course prerequisites

Basic programming skills and basic knowledge of machine learning, including standard models for classification and regression

Facts

Level
Master
Type
Mandatory course – also offered as elective
ECTS
7.5
Code
CINTO4003U
Teaching periods
Spring – semester
Academic year
2025/2026
Status
Available places
Teaching language
English
Teaching Format
Hybrid
Exams
  • Written assignment and oral exam on campus
    Group exam, summer
Assessment type
  • 7 point grading scale