Data-driven artificial intelligence

In the modern business landscape, the ability to extract valuable insights from vast volumes of data, whether structured or unstructured, is crucial for informed decision-making and maintaining competitiveness. This necessitates the utilization of data-driven AI techniques. This course serves as an introductory exploration into the key components of data-driven AI. It introduces fundamental principles while comprehensively addressing the primary methods and techniques applicable within real-world business scenarios.
The course encompasses the entire data journey, commencing with data collection and understanding, progressing through preprocessing, modeling, and evaluation. It covers machine learning techniques from both supervised learning (regression and classification) and unsupervised learning, including clustering. Topics of more recent relevance such as deep learning, conversational agent, and natural language processing, are also explored.
Furthermore, the course introduces the concept and methodologies of responsible AI, such as explainability and fairness, shedding light on its application in the context of sustainability.

The course consists of a series of lectures, lab sessions, and guest lectures. The final Ans exam is a multiple choice exam.

Upon completing the course successfully, students will be able to:

1. Discuss the key concepts of data-driven AI for business intelligence and its importance in modern business.
2. Use data visualization techniques to understand and assess the business data.
3. Set up a data mining study according to established (scientific) procedures.
4. Discuss the fundamentals of machine learning methods, such as clustering, regression, classification, deep learning, natural language processing, and responsible AI (including explainable AI and fairness).
5. Implement and evaluate data mining and machine learning solutions for business problems using Python.

You must meet one of the following collections of requirements

• Collection 1
• Completed Final examination Bsc program
• Collection 2
• Completed Pre-Master
• Collection 3
• Completed none of the course modules listed below
• Business Intelligence (1BM56)

• Compulsory (selected chapters) Han, J., Micheline K., and Jian P. (2012) Data mining: concepts and techniques. Elsevier
• Compulsory (selected chapters) Grossmann, W.; Rinderle-Ma, S. (2015) Fundamentals of Business Intelligence, Springer-Verlag, Berlin, Heidelberg.