Speaker
Pierre Schnizer
(BESSY)
Description
Machine learning has become ubiquitous today as a bracket for similar but different concepts statistical learning, neural networks, and reinforcement learning. These different approaches allow tackling a wide range of problems: deriving complex parameter sets from stochastic data, discover or simplify complex relationships, substitute diagnostics, e.g., particular beam destructive ones.
This talk will give a glimpse to the different areas of ML, recommend some tools and their usage, and describe some developments currently envisaged at BESSY II. Furthermore, it will address engineering issues that have to be addressed to roll out an ML application successfully within a large scale infrastructure.
