Remote Machine Learning for Cyberphysical Systems I & II, funded by Digitale Lehre RWTH (01.2019 - 01.2020)

With the growing use of machine learning and data analytics tools in various application the number of students interested in learning about these topics is increasing. Unfortunately, most courses and learning material focus on the theoretical part of this topic. In this project we aim to familiarize the students to data analytics techniques in the context of real world applications. Firstly, it is important for the students to create or prepare their own data as the first link in the toolchain. Furthermore, we offer insights into how some unsupervised learning techniques can be used on the datasets for our application of interest. Lastly, by keeping the material as an independent learning module and making it accessible online to RWTh students we aim to promote remote self-learning.


Alireza Zamani, Anke Schmeink

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