This course aims to apply Artificial Intelligence (AI) and Machine Learning (ML) to improve decision-making in industrial product realization, i.e. product and production development. Multi-disciplinary project teams work on solving real-world industrial challenges with data-sets provided by partner companies. Learning includes the fundamentals of AI, problem-solution mapping, data quality assessment/improvement, and organization change.
General information
WHY? The rapid advancement of digital technologies is currently reshaping the manufacturing industry. Above all, the recent breakthroughs in Artificial Intelligence (AI) and Machine Learning (ML) open enormous possibilities for improved decision-making towards sustainable manufacturing. Therefore, the demand for future engineers with multi-disciplinary competencies in developing and applying AL/ML solutions in industry has skyrocketed.
HOW? The purpose is to enable data-driven and fact-based decisions in the industrial product realization process. Therefore, the course aims to provide the students with fundamental knowledge about data science (including AI and ML) as well as skills in applying data science techniques for improving production systems and product development in a real-world context. The course applies project-based learning to create individual, personal, and flexible study opportunities for students across Chalmers.
WHAT? (course organization and learning themes) Multi-disciplinary teams collaborate on solving complex industrial problems together with manufacturing companies. Real-world needs, requirements, and data sets are provided by our partner companies. The course targets the UN Sustainable Development Goals 9 (Industry, Innovation, and Infrastructure) and 12 (Responsible Consumption and Production). The projects in the course may encompass areas of product development, production improvements, quality management, and maintenance. In addition to project-based learning, we provide lectures and workshops developing: fundamental understanding of AI/ML; communication and teamwork skills to successfully integrate key roles (e.g. data scientists and domain experts); and insights about managing the organizational change required to harness the value of AI/ML.
Prerequisites
We welcome students and alumni from all across Chalmers. We believe the course is of specific value to students from MSc programs on computer science, mathematics, automation and mechatronics, industrial, and mechanical engineering. Experience in AI/ML or product realization is beneficial. We aim to assemble multidisciplinary teams with the right competence for solving the project challenges.
How to apply
Apply to all Tracks courses at universityadmissions.se/antagning.se.
At universityadmissions.se/antagning.se: Search for the course code TRA235.
Read more here.
Include a short motivation letter where you describe your educational background, your interest and experience in data-driven product realization, and the type of project that you are interested in. Please also enclose your educational transcripts.
For alumni, PhD-stduents and professionals the course selection follows a different process. See more information on Tracks web page.
Details
Teacher (s): Jon Bokrantz, Anders Skoogh and other teachers mostly related to the Production Area of Advance at Chalmers.
Course dates: Study period 2, 2025
Credits: 7.5
Level: A
Course code: TRA235
Application deadline: