Production and Logistics Machine Learning Lab
Certificate Course - Productions & Operations Management - POM.1.3.I26
| Date | Nov 23 - 25, 2027 |
| Duration | 2,5 days |
| Location | On campus - Karlsruhe |
| Language | English |
| ECTS | upon request |
| Cost | 1,800€ |
Fundamentals
Introduces the fundamental concepts of machine learning and provides an overview of data-driven approaches for analyzing and improving production and logistics systems.
Technology
Explores key machine learning methods, including data preparation, feature engineering, time series analysis, and model evaluation, with a focus on their application in industrial environments.
Applications
Examines practical machine learning applications through real-world production and logistics cases, including predictive maintenance, data-driven problem solving, and the development of effective analytical solutions.
What you'll explore
- The fundamentals of machine learning and their application in production and logistics environments.
- Data preparation, feature engineering, and systematic approaches for analyzing industrial data.
- Time series analysis and predictive maintenance methods for data-driven production systems.
- The development and evaluation of machine learning models, including challenges such as data limitations, uncertainty, and overfitting.
- Practical machine learning applications through real-world case studies and project-based exercises using Python and scikit-learn.
- Approaches for designing, communicating, and implementing data-driven solutions for production and logistics challenges.
Your key takeaways
Participants
- Gain a practical understanding of machine learning concepts and their application in production and logistics environments.
- Learn how to prepare and analyze industrial data, including data preparation and feature engineering techniques.
- Develop the ability to design data-driven approaches for real-world challenges such as predictive maintenance and process optimization.
- Learn to critically evaluate machine learning models, identify limitations, and assess data quality, reliability, and uncertainty.
- Strengthen their problem-solving and collaboration skills through hands-on case studies and project-based machine learning applications.
Taught by recognized experts in Production and Logistics Machine Learning Lab
Benefit from the knowledge of leading specialists with extensive experience in research and industry. Their deep expertise guarantees a course of outstanding academic and practical quality.
Dr.-Ing. Fabian Rigoll
Dr.-Ing. Fabian Rigoll is Head of the Information Process Engineering research division at the FZI Research Center for Information Technology. He studied Industrial Engineering at Karlsruhe Institute of Technology (KIT) and earned his doctorate with a focus on user-centered energy data management, addressing privacy and data protection in energy informatics. His research experience spans national and international projects in energy data systems and intelligent technical communication. Since 2017, he has led research teams at FZI, focusing on intelligent information processing in complex systems. Today, he drives interdisciplinary innovation at the intersection of data, systems engineering, and applied informatics.
Dr.- Ing. Katharina Glock
Dr.-Ing. Katharina Glock is a researcher at the Karlsruhe Institute of Technology (KIT) specializing in operations research, logistics, and data-driven decision-making. Her work focuses on optimizing complex systems such as mobility, transport, and emergency response through mathematical modeling, simulation, and AI-supported analytics. She develops innovative solutions that enhance efficiency, robustness, and sustainability in logistics networks. By combining advanced optimization methods with real-world applications, she contributes to smarter, more resilient systems for future mobility and infrastructure planning.
Who should attend
This course is ideal for:
- Engineers and technical professionals who want to apply machine learning methods to real-world production and logistics challenges.
- Data analysts, process engineers, and operations professionals seeking to strengthen their skills in data-driven decision-making.
- Professionals involved in digital transformation, smart manufacturing, and the implementation of AI-based solutions in industrial environments.
- Early-career professionals and specialists who want to build practical expertise in machine learning applications for production and logistics systems.
Advance your career with KIT-level expertise
Benefit from the reputation of the Karlsruhe Institute of Technology (KIT) while gaining practical skills, flexible learning opportunities, and a recognized certificate to support your long-term professional growth
Flexibility
Gain focused expertise in a specific field without committing to a full degree program, allowing you to build relevant knowledge efficiently and integrate learning seamlessly into your professional routine.
Relevance
Benefit from high-quality academic content combined with practical insights, delivered by experienced experts, supporting continuous, lifelong learning while ensuring direct applicability in real-world scenarios.
Advancement
Enhance your professional profile with a recognized certificate, demonstrating your commitment to ongoing development and supporting your career with tangible, verifiable credentials.
About HECTOR School
HECTOR School, the Technology Business School of the Karlsruhe Institute of Technology (KIT), is a leading provider of executive education in technology-driven fields.
For this course, participants who successfully complete the examination can earn a KIT certificate with ECTS credits, which may be credited toward our Executive Master of Science or Advanced Studies Programs, subject to content alignment.