Contents:
This course gives an introduction into the physics of high-energy nucleus-nucleus collisions. The aim of this field of research is to study the strong interaction of quarks and gluons at high densities and temperatures. In particular, one would like to understand properties of the quark-gluon plasma. In this state of matter quarks and gluons are the relevant degrees of freedom and these particles are no longer confined within hadrons. In the early universe the phase transition from a quark-gluon plasma to a hadron gas took place a few microseconds after the Big Bang.

Topics covered:
kinematic variables, basics of Quantum Chromodynamics, classical string model, thermodynamics and hydrodynamics of elementary quark matter, heavy-ion physics experiments, jets and jet-quenching, charmonium as a QGP probe, production of particles with strangeness, direct photons

Please note: The time of the lecture can still be changed on request. 

This course provides a comprehensive introduction to machine learning concepts and their applications in particle physics, astroparticle physics, and astronomy. Participants will explore a wide range of topics, from foundational techniques such as machine learning classifiers and dimensionality reduction, to advanced methods including deep neural networks, generative models, and graph neural networks. Topics such as model interpretability, domain adaptation, and uncertainty quantification will also equip participants with tools to critically apply AI in research. Practical notebooks featuring real-world physics problems will enhance hands-on learning and bridge theory with practice.

”Tentative” contents:

1. A Tour of Machine Learning Classifiers
2. Dimensionality Reduction via PCA
3. Ensemble Learning
4. Shallow Neural Networks
5. Continuous Optimization
6. Deep Neural Networks
7. Convolutional neural networks
8. Recurrent Neural Networks
9. Transformers
10. Graph Neural Networks
11. Variational autoencoder
12. Generative adversarial networks
13. Normalizing Flows
14. Uncertainties quantification 
15. Anomaly detection 
16. Domain adaptation
17. Model Interpretability

Kurs im HIS-LSF