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Covers regularization in least-squares problems, promoting optimal solutions while addressing challenges like non-uniqueness, ill-conditioning, and over-fitting.
Introduces Lasso regularization and its application to the MNIST dataset, emphasizing feature selection and practical exercises on gradient descent implementation.
