The school aims at PhD students and young postdocs, and offers talks from leading experts in the field on their current research projects, and some exercises. The list of lectures comprises:

  • Network architectures with a focus on Graph networks
  • Generative Models
  • Normalising Flows
  • Weak classification and anomaly detection
  • Machine learning on FPGAs

In order to participate successfully in the school, we expect the following prerequisites:

  • Basic knowledge of Python programming: defining variables, writing functions, importing modules.

Some prior experience with the NumPy, pandas and Matplotlib libraries is recommended but not required.

For a quick introduction on these requirements, you can go through these course materials or use the following resources:


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The school is supported by the Excellence Cluster Quantum Universe Hamburg.

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