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Advanced Optical Technologies (Master of Science) >>
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Maschinelles Lernen und Datenanalytik für Industrie 4.0 (MADI40)- Lecturers
- Prof. Dr. Björn Eskofier, Johannes Roider, M. Sc., Christoph Scholl, M. Sc., Lukas Schmidt, M. Sc.
- Details
- Seminar
2 cred.h, benoteter certificate, ECTS studies, ECTS credits: 5
nur Fachstudium, für FAU Scientia Gaststudierende zugelassen, Sprache Englisch, Registration via mail to johannes.roider@fau.de
Time and place: Wed 16:15 - 18:00, 00.010; comments on time and place: Starts April 27th 2022
- Fields of study
- WPF MT-MA-BDV ab 1 (ECTS-Credits: 5)
WPF INF-MA ab 1 (ECTS-Credits: 5)
WPF CE-MA ab 1 (ECTS-Credits: 5)
WF ASC-MA ab 1 (ECTS-Credits: 5)
WF ICT-MA ab 1 (ECTS-Credits: 5)
WPF AI-MA ab 1 (ECTS-Credits: 5)
- Prerequisites / Organisational information
- Registration via e-mail to johannes.roider@fau.de Registration period: 25.02.-04.05.2022
The seminar will be held face-to-face.
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
Please state your previous experience in machine learning (e. g. Which courses did you take? Which project experience do you have?) when registering for the course. Examination:
50% of grade: Presentation + demo (20 minutes)
50% of grade 4 pages IEEE standard paper (excluding references) (+ code submission)
Attendance of all meetings is required.
- Contents
- Contents
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 adjacent fields like the medical device or the automotive sector. Aim of this seminar is to give students insights about state-of-the-art machine learning and data analytics methods and applications in Industry 4.0 and adjacent fields. Students will mainly work independently on either a implementation centric or a research centric topic. The implementation centric topics will focus primarily on the implementation of algorithms and analytical components, while the research centric topic will focus on researching and structuring literature on a specific field of interest. 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
Brief 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, ...)
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. 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 / automotive
Students will learn to research and present a topic within the context of machine learning and data analytics for Industry 4.0 / healthcare / automotive independently
Students will learn to identify opportunities, challenges and limitations of corresponding ML approaches for Industry 4.0 / healthcare / automotive
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
- Recommended literature
- 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.
- ECTS information:
- Title:
- Machine Learning and Data Analytics for Industry 4.0
- Credits: 5
- Additional information
- Keywords: Machine Learning, Data Analytics, Process Mining, Predictive Maintenance, Industry 4.0, Healthcare, Automotive
Expected participants: 7, Maximale Teilnehmerzahl: 10
Registration is required for this lecture.
Registration starts on Friday, 25.2.2022 and lasts till Wednesday, 4.5.2022 über: persönlich beim Dozenten.
- Verwendung in folgenden UnivIS-Modulen
- Startsemester SS 2022:
- Seminar Machine Learning and Data Analytics for Industry 4.0 (SemMLDA)
- Department: Lehrstuhl für Maschinelles Lernen und Datenanalytik
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