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Explores kernels for simplifying data representation and making it linearly separable in feature spaces, including popular functions and practical exercises.
Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.
Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.
