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Explores Kernel Ridge Regression, the Kernel Trick, Representer Theorem, feature spaces, kernel matrix, predicting with kernels, and building new kernels.
Explores the application of machine learning in molecular dynamics and materials, emphasizing the creation of meaningful features and the importance of generalizability.
Explores gradient descent methods for smooth convex and non-convex problems, covering iterative strategies, convergence rates, and challenges in optimization.
