Lecture
This lecture covers the spectral estimation problem for Gaussian and binary signals, focusing on the spiked matrix estimation problem. It discusses the computation of mean-squared error, self-consistent equations, and the performance of principal component analysis. The lecture also explores the impact of signal-to-noise ratio on the solutions, the transition between recovery phases, and the use of damping in iterations for convergence.