Code Demos for "Regression on Ice" Lecture Notes

By Noah J Bergam

Notebooks with visualizations of some basic regression / machine learning concepts for glaciology

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Version 1.1 - published on 11 Oct 2023

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Modern satellite-based analysis of the ice sheets presents a profound statistical-geometric problem: how do we make sense of scattered, noisy measurements of vast, steadily evolving surfaces like the Greenland and Antarctic ice sheets? In these lecture notes, I attempt to provide the mathematical foundations of function approximation techniques that may aid the reader in appreciating and tackling this problem. The main topics include non-parametric regressionlinear modelsGaussian processes, and reproducing Kernel Hilbert spaces. Each chapter features both examples from recent glaciology research and mathematical “curios” which invoke more niche remarks, such as the duality of Voronoi and Delaunay graphs and the intimate relationship between free knot linear splines and ReLu

The accompanying lecture notes are available at The Ghub.


Code is also available in a GitHub repository.

Cite this work

Researchers should cite this work as follows:

  • Noah Bergam. (2023, September 20). Regression on Ice: Function approximation for the mathematically-inclined glaciologist. Zenodo.
  • Noah J Bergam (2023), "Code Demos for "Regression on Ice" Lecture Notes,"

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