Brummer & Partners MathDataLab mini course:

Randomized methods in linear algebra and their applications in data science

Lecturer: Per-Gunnar Martinsson (Univ. of Texas at Austin)

Course objectives: The lectures will describe a set of recently developed randomized algorithms for accelerating matrix computations and the analysis of large data sets. A recurring theme will be the use of randomized embeddings that reduce the effective dimensionality of data sets while in certain respects preserving their geometric properties. We will describe how to use the methods in practice, and how their performance can be analyzed mathematically.

Format: Three lectures delivered via Zoom, at 15:15 - 16:00 (CET), on November 17, 18, 19.

Target audience: The lectures will be self-contained, and will in principle assume only knowledge of basic material on linear algebra and probability theory.

Course timeline/content: See flyer.

Lecture slides: pdf.

Surveys and articles: Several surveys on randomized linear algebra are available, see Section 1.7 of this paper for a partial list. The following papers follow the presentation in these lectures closely: Software:

Research support by:

P.G. Martinsson, November 2020