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Seminar Machine Learning and Data Analytics for Industry 4.0 (SemMLDA)5 ECTS (englische Bezeichnung: Seminar Machine Learning and Data Analytics for Industry 4.0)
(Prüfungsordnungsmodul: Seminar Machine Learning and Data Analytics for Industry 4.0)
Modulverantwortliche/r: An Nguyen Lehrende:
An Nguyen, Björn Eskofier, Johannes Roider, Christoph Scholl
Startsemester: |
SS 2022 | Dauer: |
1 Semester | Turnus: |
jährlich (SS) |
Präsenzzeit: |
30 Std. | Eigenstudium: |
120 Std. | Sprache: |
Englisch |
Lehrveranstaltungen:
Inhalt:
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 (provided or open source datasets).
Lernziele und Kompetenzen:
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
Literatur:
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): 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.
• 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.
Organisatorisches:
Registration via e-mail to johannes.roider@fau.de
Verwendbarkeit des Moduls / Einpassung in den Musterstudienplan:
- Artificial Intelligence (Master of Science)
(Po-Vers. 2021s | TechFak | Artificial Intelligence (Master of Science) | Gesamtkonto | Hauptseminar | Seminar Machine Learning and Data Analytics for Industry 4.0)
Dieses Modul ist daneben auch in den Studienfächern "Advanced Optical Technologies (Master of Science)", "Computational Engineering (Rechnergestütztes Ingenieurwesen) (Bachelor of Science)", "Data Science (Bachelor of Science)", "Data Science (Master of Science)", "Informatik (Bachelor of Arts (2 Fächer))", "Informatik (Bachelor of Science)", "Informatik (Master of Science)", "Information and Communication Technology (Master of Science)", "Informations- und Kommunikationstechnik (Master of Science)", "Medizintechnik (Master of Science)" verwendbar. Details
Studien-/Prüfungsleistungen:
Seminar Machine Learning and Data Analytics for Industry 4.0 (Prüfungsnummer: 903776)
(englischer Titel: Seminar Machine Learning and Data Analytics for Industry 4.0)
zugeh. "mein campus"-Prüfung: | - 903776 Seminar Machine Learning and Data Analytics for Industry 4.0 (Gewichtung: 100.0 %, Prüfung, Form: Seminarleistung, Zehntelnoten, Dauer: -, 5 ECTS, Prüfung).
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- Prüfungsleistung, Seminarleistung, benotet, 5 ECTS
- Anteil an der Berechnung der Modulnote: 100.0 %
- weitere Erläuterungen:
Final presentation with demo (20 minutes + 5 minutes discussion, 50% of grade) and paper according to IEEE standards (4 pages excluding references, 50% of the grade)
- Erstablegung: SS 2022, 1. Wdh.: WS 2022/2023
1. Prüfer: | Björn Eskofier (100122) |
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