Lecture
This lecture covers the concept of graphical model learning using M-estimators, focusing on statistical properties and empirical covariance matrices. It also delves into Gaussian process regression for kernel hyperparameter tuning, Google PageRank modeling, density estimation, and generalized linear models. The instructor emphasizes the importance of optimization formulations, least-squares estimators, and risk minimization settings in various machine learning applications.