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Subject offerings of new and developing areas of knowledge in physics intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

This course covers modern physics-inspired mathematical, statistical and Monte Carlo methods for analyzing scientific data. Topics to be covered include review of linear algebra and Hilbert space, spectral graph theory, clustering methods, dimensional reduction techniques, Reproducing Kernel Hilbert Space, kernel embedding, Grassmannian manifolds, matrix and tensor decompositions, stochastic sampling methods, numerical optimization, cross entropy method, Markov Chain Monte Carlo, and Gaussian Process.