Institute Seminar | Steve Schmerler | 2022-11-08 | 2/2 Discussion
Channel: CASUS.science
Date: 05/12/2022

TITLE: Introduction to kernel methods and Gaussian processes by Dr. Steve Schmerler, Helmholtz AI Consultant, Consultant Team “Matter”, Helmholtz-Zentrum Dresden-Rossendorf (HZDR)

Steve holds a PhD in theoretical solid state physics. Nowadays, he is part of Helmholtz AI working at the interface between physics and machine learning. His projects cover machine learning surrogate models for complex simulations, uncertainty quantification methods to make neural network models more trustworthy as well as classical machine learning such as kernel methods and Gaussian processes.

ABSTRACT:
Kernel ridge regression (KRR) is an example of a kernel method. It represents the family of “classical” machine learning techniques (e.g. no neural networks) that is popular in application areas where data is not necessarily abundant and where one can build expressive models by using (physics-informed) engineered features. A closely related method is Gaussian process regression (GPR), whose foundations lie in Bayesian statistics and which can provide powerful uncertainty information. In this introduction, Steve will cover the mathematical foundations of those methods, shed light onto their close relation and discuss when they may be a useful parallel approach to more flexible models such as neural networks.

LICENSE:
CC BY-SA 4.0

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