Explores graphical model learning with M-estimators, Gaussian process regression, Google PageRank modeling, density estimation, and generalized linear models.
Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.