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Introduces Lasso regularization and its application to the MNIST dataset, emphasizing feature selection and practical exercises on gradient descent implementation.
Explores non-linear SVM using kernels for data separation in higher-dimensional spaces, optimizing training with kernels to avoid explicit transformations.
Discusses kernel methods in machine learning, focusing on kernel regression and support vector machines, including their formulations and applications.
