Predictive Analytics for Production Systems

Fact-Sheet

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Registration via SPUR by 17.10.2024

Term

Winter term

Language

English

Applicability

• Industrial Management

• Business Analytics and Operations Management 

• Electives modul

Course content

This course introduces descriptive and predictive Artificial Intelligence (AI) approaches for different Operations Management problems. In particular, machine learning approaches for unsupervised and supervised learning are introduced. For example, Neural Networks are presented to predict and optimize the performance of operations systems based on data. Applications in the areas of production management, maintenance, and yield prediction are discussed.
An introduction to the basics of programming with Python is provided. This is the basis for own applications and implementations of AI approaches by the students. Moreover, the students will leverage libraries of AI approaches. During the course, the students will work on several case studies and assignments (individually or in groups).

Learing goals

• Students will be familiar with the fundamental concepts of different AI approaches.

• Students will learn how to select suitable AI techniques to obtain insights from big data sets of real-world problems to make business decisions supported by the data.

• Students will also develop programming skills that allow them to implement and apply AI approaches.

Grading

Casework (written reports, group presentations)

Written exam, 45 minutes