This course provides a rigorous introduction to mathematical tools for data science drawn from linear algebra, Fourier analysis, probability theory, and convex optimization. The main topics are covariance matrices, principal component analysis, linear regression, regularization, sparse regression, frequency representations, the short-time Fourier transform, wavelets, Wiener filtering, and convolutional neural networks. The material is illustrated by multiple applications to real data.
We gratefully acknowledge the support of the National Science Foundation through awards DMS-2009752, HDR-1940097 and NRT-HDR 1922658.
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Mathematical Tools for Data Science by New York University, Carlos Fernandez-Granda is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Based on a work at https://cds.nyu.edu/math-tools/.