Requirements:

• 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

Examination:
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

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

• 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): 799–834.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): 224–36. 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.
  

Credits: 5