Teaching
Machine learning in quantum physics  winter 18/19
Quantum machine learning is an emerging research field.
This first course focuses on the subfield where classical machine learning is applied in quantum physics.
Content
Introduction to neural networks and deep learning
Applications in quantum physics
Formal things
Parts of 2. will be given by the students in form of seminar talks
The course consists of 2 hours lecture and 1 hour tutorial class per week.
There will also be assignments, partially with simple coding exercises.
Prerequisites are linear algebra and quantum mechanics.
Note
This course outline is a preliminary plan and still subject to changes.
Comments and suggestions are welcome (info@mkliesch.eu).
References
