Maschinelles Lernen und Datenanalytik für Industrie 4.0
Time and place:
Registration via mail to email@example.com
- Wed 16:15-18:00, Room 00.010
Fields of study
- WPF MT-MA-BDV from SEM 1
- WPF INF-MA from SEM 1
- WPF CE-MA from SEM 1
- WF EEI-MA from SEM 1
- WF ASC-MA from SEM 1
- WF ICT-MA from SEM 1
Prerequisites / Organizational information
Registration via e-mail to firstname.lastname@example.org Registration period: 03.02.2020 - 22.04.2020
This course will be held online until the coronavirus pandemic is contained to such an extent that the Bavarian state government can allow face-to-face teaching again. More information will be sent via mail to registered students.
- Prior knowledge of machine learning via courses like PA, IntroPR, PR, DL, MLTS, CVP or equivalent (ideally first project experiences) is expected!
- Motivation to explore scientific findings (e.g. via literature research)
- Motivation to code and analyze data
50% of grade: Presentation + demo (25 minutes)
50% of grade 4 pages IEEE standard paper (excluding references) + code submission
Attending the presentations of other students
Companies in all kinds of industries are producing and collecting rapidly more and more data from various sources. This is enabled by technologies such as the Internet of Things (IoT), Cyber-physical system (CPS) and cloud computing. Hence there is an increasing demand in industry and research for students and graduates with machine learning and data analytics skills in the Industry 4.0 context.
In this Seminar the Industry 4.0 term will include the medical device sector. Aim of this seminar is to give students insights about state-of-the-art machine learning and data analytics methods and applications in the Industry 4.0 and Healthcare context. Students will mainly work independently on specific topics including implementation and analytical components. Several potential topics will be provided but students are also encouraged to propose their own topics (please discuss with teaching staff beforehand).
Topics covered will include but are not limited to:
- Best practices for presentation and scientific work
- Overview of current hot topics in the field of machine learning and data analytics for Industry 4.0 (e.g. deep learning for predictive maintenance and process mining for usage analysis)
- Data acquisition (what kind of data can be acquired? Identification of publicly available data sets) and storage (how can data be stored efficiently?)
- Machine learning and data analytics methodologies (Support vector machines, Hidden Markov models, Deep learning, Process mining, etc.) for industrial data (sensor data, event logs, ...)
- Object detection in industry application
The seminar will include talks by corresponding lecturer and invited experts in the domain. Furthermore, students will present results from literature research and data analytics projects (provided or open source datasets).
Learning Objectives and Competencies
- Students will develop an understanding of the current hot field of machine learning and data analytics for Industry 4.0 / healthcare
- Students will learn to research and present a topic within the context of machine learning and data analytics for Industry 4.0 / healthcare independently
- Students will learn to identify opportunities, challenges and limitations of corresponding ML approaches for Industry 4.0 / healthcare
- Students will develop the skill to identify and understand relevant literature and to present their finding in a structured manner
- Students will learn to present implementation and validation results in form of a demonstration and/or report
*Literature (Selection)* - Lei, Yaguo, Naipeng Li, Liang Guo, Ningbo Li, Tao Yan, and Jing Lin. "Machinery Health Prognostics: A Systematic Review from Data Acquisition to RUL Prediction. Mechanical Systems and Signal Processing 104 (May 2018): 799834.https://doi.org/10.1016/j.ymssp.2017.11.016. - Rojas, Eric, Jorge Munoz-Gama, Marcos Sepúlveda, and Daniel Capurro. "Process Mining in Healthcare: A Literature Review. Journal of Biomedical Informatics 61 (June 1, 2016): 22436. https://doi.org/10.1016/j.jbi.2016.04.007. - Wil M. P. van der Aalst. Process Mining: Data Science in Action 2nd edition, Springer 2016. ISBN 978-3-662-49851-4 - Wang, Lihui, and Xi Vincent Wang. Cloud-Based Cyber-Physical Systems in Manufacturing. Cham: Springer International Publishing, 2018. https://doi.org/10.1007/978-3-319-67693-7.
Keywords: Machine Learning, Data Analytics, Process Mining, Predictive Maintenance, Industry 4.0, Healthcare
Expected participants: 6