Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.
Covers the concept of gradient descent, a universal algorithm used to find the minimum of a function.
Covers upper and lower bounds, Jensen's inequality, and amended bounds in mathematical analysis.
Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.
Covers the concept of symmetry in physics and its applications in various contexts.
Covers the concept of kernel regression and making data linearly separable by adding features and using local methods.
Introduces Lasso regularization and its application to the MNIST dataset, emphasizing feature selection and practical exercises on gradient descent implementation.
