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Explores the Bayesian extension of HMM for robot action segmentation and modeling, limitations of classical HMMs, and motion capture data segmentation.
Explores Kernel Ridge Regression, the Kernel Trick, Representer Theorem, feature spaces, kernel matrix, predicting with kernels, and building new kernels.
Explores Stochastic Gradient Descent with Averaging, comparing it with Gradient Descent, and discusses challenges in non-convex optimization and sparse recovery techniques.
