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2023/2024  KAN-CINTO4003U  Artificial Intelligence and Machine Learning

English Title
Artificial Intelligence and Machine Learning

Course information

Language English
Course ECTS 7.5 ECTS
Type Mandatory (also offered as elective)
Level Full Degree Master
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, MSc
Course coordinator
  • Daniel Hardt - Department of Management, Society and Communication (MSC)
Main academic disciplines
  • Information technology
  • Innovation
Teaching methods
  • Blended learning
Last updated on 13-12-2023

Relevant links

Learning objectives
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 machine learning techniques in a variety of models, including linear models, tree models, ensembles, and neural networks.
  • Demonstrate a practical ability to apply these machine learning techniques to relevant business cases, especially involving core AI application areas such as natural language processing and image processing.
  • Understand basic principles of machine learning, including assessment of results using a variety of metrics, comparison of models, grid search and other techniques for tuning of models, and pipelines.
Course prerequisites
Basic programming skills and basic knowledge of machine learning, including standard models for classification and regression
Prerequisites for registering for the exam (activities during the teaching period)
Number of compulsory activities which must be approved (see section 13 of the Programme Regulations): 2
Compulsory home assignments
Each student has to get 2 out of 3 home assignments approved in order to participate in the ordinary exam. The assignments are written individually and are max. 5 pages each.

There will not be any extra attempts provided to the students before the ordinary exam. If a student cannot participate due to documented illness, or if a student does not get the activities approved in spite of making a real attempt, then the student will be given one extra attempt before the re-exam. Before the re-exam, there will be one home assignment (10 pages) which will cover 2 mandatory assignments.
Artificial Intelligence and Machine Learning:
Exam ECTS 7,5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance, see also the rules about examination forms in the programme regulations.
Individual or group exam Oral group exam based on written group product
Number of people in the group 2-5
Size of written product Max. 15 pages
Assignment type Project
Release of assignment Subject chosen by students themselves, see guidelines if any
Written product to be submitted on specified date and time.
20 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
Students can submit the same project or they can choose to submit a revised project.
Course content, structure and pedagogical approach

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.

Description of the teaching methods
Lectures and weekly hands-on sessions with practical exercises.
Feedback during the teaching period
Students have hands-on exercises each week, where they receive in-person feedback from the teacher. They also receive feedback from regular online elements such as quizzes. Furthermore, they receive weekly written feedback on their work. Mid-way through the course, they create a plan for their project, and they receive feedback from the professor on their plan. There are also weekly office hours.
Student workload
Lectures 20 hours
Exercises 10 hours
Class Preparation 116 hours
Exam and Preparation for Exam 60 hours
Total 206 hours
Expected literature

The literature can be changed before the semester starts. Students are advised to find the literature on Canvas before they buy the books.


Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: a guide for data scientists.  O'Reilly Media, Inc.


Last updated on 13-12-2023