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Numerical Linear Algebra

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Machine Learning algorithms rely fundamentally on concepts of numerical mathematics. While many software packages try to obscure the computational details underlying numerical operations, the increasing size and complexity of data sets makes it critical that researchers pay attention to numerics in their algorithms. We will give an overview of basic concepts in numerical linear algebra as they pertain to machine learning algorithms. We will also describe the computational realities of modern computers and software platforms. Together, these two topics will provide researchers the tools to ask questions about how to improve the efficiency, run-time, stability, and memory usage of their algorithms.

This talk is part of the Machine Learning Reading Group @ CUED series.

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