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:
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representation learning (including deep variational autoencoders etc.)
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active learning (how a neural network can choose suitable training samples on its own)
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reinforcement learning and optimization methods
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graph neural networks
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generative neural networks (learning to sample from an observed statistical distribution)
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transformers and other attention-based methods
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advanced concepts from information science and statistics (e.g. mutual information)
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automated program discovery
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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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