AI for Business

AI for Business


AI is changing the ways organizations are performing and making decisions. This course aims to provide an understanding of this tool and this change in the context of business. 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.

Course objectives

The course provides students with practical knowledge of using AI techniques and different scientific management frameworks to tackle important business problems. At the end of the course, students will be able to:

  • to draw insight about internal and external factors that influence innovation and strategy process, using Python;
  • to predict the outcome of different scenarios or propositions, using Python;
  • to employ techniques to learn systematically from past events;
  • to describe the differences between different analytical approaches for describing, predicting, and explaining.


Semester 1:

  • Course: November 9th - December 9th 2020
  • Exam: December 16th 2020
  • Retake: January 20th 2021

Please check the latest version of the schedule on the SBB website.

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 includes a mix of individual and group assignments and final exam. The details are as follows:

  • Course homework: 15%
    • This component include practices from DataCamp.
  • Class participation: 10%
  • Group Project: 35%
  • Final Exam: 40%

Reading list

The reading list will be announced on Brightspace.