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2020/2021  KAN-CINTO1820U  Artificial Intelligence and Robotics

English Title
Artificial Intelligence and Robotics

Course information

Language English
Course ECTS 7.5 ECTS
Type Mandatory 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 22-06-2020

Relevant links

Learning objectives
  • Demonstrate the ability to understand and implement machine learning techniques, including classification and regression
  • Demonstrate a practical ability to apply these machine learning techniques to relevant business cases
  • Understand basic theoretical principles of machine learning, including assessment of results, comparison of models, and tuning of models
  • Reflect on the societal and business impact of AI technologies
Course prerequisites
Basic programming skills and knowledge of machine learning
Artificial Intelligence and Robotics:
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.
Individual or group exam Individual oral exam based on written group product
Number of people in the group 2-5
Size of written product Max. 15 pages
Assignment type Project
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 business impacts of these technologies are also considered. The course will start with an introduction to machine learning and then proceed to work on data set that can be used in the final project. 

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 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.


Alan Turing (1950).  Computing Machinery and Intelligence. Mind 49: 433-460.


Andreas C. Müller and Sarah Guido (2016).Introduction to Machine Learning with Python: A Guide for Data Scientists. O'Reilly Media, Inc.


Thomas H. Davenport (2018). The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. The MIT Press.

Last updated on 22-06-2020