Resource-aware Machine Learning

About

The 6th International Summer School on Resource-Aware Machine Learning (REAML 2022) provides lectures on the latest research in machine learning, typically with a twist on resource consumption and how these can be reduced.

The Summer School will be offered as a hybrid event. Due to the ongoing COVID-19 pandemic, it is not guaranteed that every international participant/lecturer can visit Dortmund. The event will thus be a mixture of local and (possibly some) remote lectures. All lectures will also be streamed via Zoom and Youtube to the remote audience of participants that could not travel to Germany. Lectures will be available on-demand on YouTube during the week of the Summer School. Each lecture will be accompanied by a Q&A session. There will be a dedicated space for presenting Ph.D./PostDoc research and a hackathon featuring real-world ML tasks.

A selection of course topics: Efficient federated learning, From k-means to deep learning, The generalization mystery in deep learning, Reproducible data analysis,
Understanding inverse problems

Hackathon
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The summer school is accompanied by a hackathon about "Predicting Virus-Like Particles in Liquid Samples". Fitting the context of the COVID-19 pandemic, participants are challenged with the detection of nanoparticles such as viruses. Using a plasmon-assisted microscopy sensor that can make nanometer-sized particles visible, we provide real-world images containing virus-like signals. The participants are challenged to test their knowledge of Machine Learning and cyber-physical systems in this real-world scenario. In this hackathon, they aim to achieve the most reliable and rapid detection possible with limited resources.

Students’ Corner - Share and discuss your work
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The summer school will be accompanied by an exchange platform for participants, the Students' Corner, which will allow them to network and share their research.

Course leader

The summer school is organised by the collaborative research center SFB 876 and the artificial intelligence group at TU Dortmund University.

Target group

PhD students/PostDocs with a background in machine learning. Industry practitioners working as data scientists, machine learning experts or engineers applying machine learning.

Course aim

The International Summer School on resource-aware Machine Learning brings together lectures from the research area of data analysis (Machine Learning, data mining, statistics) and embedded systems (cyber-physical systems). It aims at taking into account the constraint of limited resources of host devices used for data analysis.

Fee info