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| CALD 10-602 builds on the material presented in 10-601, introducing new learning methods and going more deeply into their statistical foundations and computational aspects. Topics include discriminative vs. informative learning, data augmentation algorithms, Markov chain Monte Carlo, the method of alternating projections, and feature selection techniques. Applications and case studies from statistics and computing are used to illustrate each topic, together with aspects of implementation and practice. |