Winter term 2021/22

Advanced Machine Learning for Physics and Scientific Discovery

  • Lecturer: Florian Marquardt
  • 5 ECTS credit points
  • Two 90min lectures per week
  • Mondays and Wednesdays, 18:00 - 19:30 online
  • written exam

We will describe advanced modern methods of artificial intelligence and their potential application to artificial scientific discovery, in physics and other fields. This includes:

  • representation learning (including deep variational autoencoders etc.)

  • active learning (how a neural network can choose suitable training samples on its own)

  • reinforcement learning and optimization methods

  • graph neural networks

  • generative neural networks (learning to sample from an observed statistical distribution)

  • transformers and other attention-based methods

  • advanced concepts from information science and statistics (e.g. mutual information)

  • automated program discovery

  • applications in quantum physics, statistical physics, dynamical systems

For more information, e. g. on registration for the course and a course summary, please visit the lecture website.

Bachelor Theory Colloquium

For information (in German) on the bachelor theory colloquium, please refer to  ► exam info

Teaching archive

Seminars and lectures of past semesters can be found in our ► teaching archive

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