2023-04-11 - 2023-04-14
Claudius Krause
Heidelberg University
Modern machine learning is becoming a standard tool in our numerical tool box. Not only can neural networks boost the performance of existing algorithms, they can also enable new analysis or simulation strategies. I will introduce the basics of modern machine learning and then focus on specific applications such as (bayesian) regression, classification, generative models (especially normalizing flows), and parameter inference. My examples will be chosen from particle physics publications of the last few years, but the methods can be applied to a much broader set of (research) questions.
