Syllabus
1. Automatic differentiation for Machine learning in programming languages Python and Julia.
2. Neuromorfic computing. Basic concepts and current state of the research in the field.
3. TensorFlow Quantum.
4. Neural network quantum states beyond RBM.
5. Automatic phase classification via network confusion.
6. Physics-informed neural networks.
7. Graph neural networks.
Annotation
We will address several advanced topics of Machine Learning for physical applications which go beyond standard introductory courses. Each lesson will consist of a 45-60 minute long lecture followed by practical examples and discussion.
The students will be encouraged to prepare a short talk, essay or a worksheet (i.e. a jupyter notebook) on a particular topic. The seminar is intended for students familiar with the basics of machine learning techniques who are interested in current development in the field.