Lectures related to Compressed sensing

Explores the Spike Wigner model, Bayesian denoising, state evolution, and spectral methods in matrix analysis.

Explores primal-dual optimization with a focus on Lagrangian methods and their convergence, drawbacks, and enhancements.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Explores matrix factorization techniques for recommender systems, including evaluation metrics like RMSE and NDCG.

Explores the geometric explanation of why Lasso solutions are sparse and how coefficients change with the regularization parameter.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Explores matrix factorization in recommender systems, covering optimization, evaluation metrics, and challenges in scaling.