The topic of this project is the efficient training of Machine Learning by Kernel Ridge Regression.
Relevant content will be:
- Kernel Ridge Regression
- iterative solvers for linear systems
- matrix approximation techniques:
- low rank approximation (SVD, ACA, …)
- Hierarchical Matrices
Application data should be large-scale and science-related. Maybe the first starting point would be data from quantum chemistry that I have access to.
The beauty of this project would be to further develop and analyze the impact of non-exact solvers for linear systems on the quality of the prediction of Kernel Ridge Regression. This is highly research relevant.
WARNING: Some flavor of this topic (e.g. hierarchical matrices) requires a profound mathematical background.
Some first links: