Design of AI-driven business operation

During the course, the students will analyze and redesign a business operation, based on data from practice. The students will learn about methods that are available to guide the redesign of an operation in a stepwise manner. Subsequently, each week they will study techniques that can be applied in the various steps and apply these techniques to the dataset. Students will be assisted in performing these tasks in an introductory lecture, working sessions, and through office hours that are kept by the lecturers.

At the end of the course:

• the student can apply a suitable data-driven design methodology (e.g., CRISP-DM) to address a particular design problem;

• the student can refine methods and techniques from scientific papers to solve parts of a design problem;

• the student can improve a business operation from practice, using the methodology, methods and techniques from AI, especially data mining, machine learning, and optimization;

• the student can report on a design and the procedure followed to arrive at that design, using both an oral and a written report..

• the student can collaborate in a group to achieve all of the above.

Additional information tests
Students can resit the project and final report by improving the previous work and implementing new tasks that are specified by lecturers.
Students cannot resit the presentations.

You must meet all the following collections of requirements

• Collection 1
• Completed Final examination Bsc program
• Collection 2
• Completed Pre-Master

• Selected scientific papers (accessible via TU/e library) to be made available in the course syllabus