Data-driven artificial intelligence

Modern businesses need to make sense of vast amounts of (structured or unstructured) data in order to support business decisions, and remain competitive. Data-driven AI techniques address this purpose. It comprises the strategies and technologies used by organizations for the data analysis of business information. It provides insight into the business activities to support decision making.
This course provides an introduction to the major aspects of data-driven AI. It introduces the fundamental concepts, and covers the main methods and techniques to enable applications in real-world business context. It starts with the data provisioning process, ranging from data collection and preparation to modelling, evaluation and deployment, and continues with the approaches for data visualization. Next, analytical techniques for supervised learning (prediction and classification) and unsupervised learning (clustering) are introduced. Topics of more recent relevance such as deep learning and text mining are also discussed. Furthermore, the concept of explainable AI is introduced and some related techniques are discussed.

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

• discuss the key concepts of data-driven AI for business intelligence and its importance in modern business,
• use data visualization techniques to understand and assess the business data,
• set up a data mining study according to established (scientific) procedures,
• understand various data mining and machine learning methods, including clustering, regression, classification, deep learning, and natural language processing techniques.
• apply various data mining and machine learning techniques to business problems using appropriate tools (e.g., Python).
• understand and apply the concept/methods of explainable AI.

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)

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