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AI for Business (minor Science, Business, and Innovation)


Admission requirements

Third-year Bachelor students


Artificial intelligence (AI) is changing the ways organizations are performing and making decisions. This course analyzes relevant aspects of AI and how AI influences firms decisions and stakeholders. This course introduces some of the most popular AI tools, focusing on these methods' intuitions and exploring their business applications to real-life business cases. This course adopts a hands-on approach in implementing these AI tools using Python, a powerful programming language used widely to tackle and solve machine learning problems. To sum, the course helps students to understand, analyze, and tackle business problems using AI tools.

Note: this course uses Python as the coding language for all the sessions, the group project, and the individual assignment. Therefore, completing the course requires a knowledge of Python. The course does not explicitly teach how to code in Python; rather, it focuses on applying AI techniques to business problems, using Python. The course does not require students to know Python ahead of time, but the students need to learn this language during the class — there will be ample learning resources. This might be an immense effort for people who don't have any coding experience.

Course objectives

Upon successful completion, students will be able to:

  • tunderstand how AI is changing the business landscape;

  • analyze AI impacts on our understanding of business;

  • evaluate AI impacts on some of the key stakeholders;

  • use machine learning techniques to solve business problems.


Check MyTimetable and use your ULCN account to login.

You will find the timetables for all the courses and degree programme in MyTimetable. This enables you to create a personal timetable. Any teaching activities that you have registered for in uSis will automatically be displayed in your timetable. Any timetables that you add will be saved and automatically displayed the next time you sign in.

Mode of instruction

The course emphasizes interactive teaching. Classes start with a brief reflection on the past sessions. Then, it reviews the session’s reading(s) and discusses relevant AI methods. In the second half, the lecture focuses on a real-life case and uses Python to explore the session’s topic in practice. The session ends with a summary.

Assessment method

The course assessment details are as follows:

  • Class participation: 10%

  • Group Project: 45%

  • Final individual assignement: 45%

Reading list

The reading list will be announced on Brightspace.


Students have to register for the course in uSis. Click here for instructions.

This course can only be followed as part of the SBI minor (15 or 30 ECTS).


Registration for courses is via uSis. When you register here for a certain course, you automatically receive access to the environment of this course via Brightspace.

For more information about Brightspace, you can click on this link to view the university manuals. For other questions or problems, you can contact the helpdesk of Leiden University.



  • Students are responsible for enrolling/unenrolling themselves for (partial) exams/retakes.

  • Students are responsible for enrolling themselves for (partial) exams/retakes.

  • The deadline for enrolling for an exam/retake is 14 calendar days before the exam/retake takes place (exam date - 14 = deadline enrolling date).

  • Students who do not enroll themselves for an exam/retake by the deadline are not allowed to take the exam/retake.

  • Students fail the course if any of the partial components (except the exam) that make up the final mark of the course is assessed below 4.0.

  • Students fail the course if the grade for the (final) exam is assessed below 5.0.

  • The final grade is expressed as a whole or half number between 1.0 and 10.0, including both limits. The result is not to be expressed as a number between 5.0 and 6.0.

  • If one of the components of the final mark constitutes a component that assesses attendance or class participation, students cannot take a retake for this component. Therefore, students fail the course if their mark for this component is less than 4.0.

  • Partial grades, inclusive the exam grade will not be rounded. If partial grades will be communicated, it is possible partial grades are rounded, but unrounded partial grades will be used in the calculation of the final grade. The final grade will be rounded at 0.5 (5.49 will rounded down to a 5 and a 5.5 will be rounded up to a 6.0).

  • It is not possible to do retakes for group assignments. Therefore, if students fail the group assignment component, they fail the course.

  • Students pass the course if the final mark is 6.0 or higher (5.49 will rounded down to a 5 and a 5.5 will be rounded up to a 6.0).

  • For courses, for which class participation is an assessment component, students may not be penalised for an absence if the student has a legitimate justification for this absence. The student must notify the program coordinator via email ( of such an absence BEFORE the lecture, describing the reason for missing the lecture. If the student does not notify the program coordinator before the lecture, the student will be penalised. Students may be required to provide further documentation to substantiate their case, and class attendance requirements are only waived under exceptional circumstances such as illness.

  • Students who are entitled to more exam/retake time must report to 10 days before the exam/retake takes place.