New York University 3 credits Data Science
This course provides a rigorous introduction to mathematical tools for data science drawn from linear algebra, harmonic analysis, probability theory, and convex analysis. The main topics are the singular-value decomposition (SVD), the Fourier series, randomized projections, the randomized SVD, convex optimization, duality theory and nonconvex optimization. The material is motivated by multiple data-analysis applications including dimensionality reduction, collaborative filtering, sound and image processing, magnetic-resonance imaging, sparse regression, compressed sensing, and topic modeling.
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