Covers linear problems, LASSO, and AMP in supervised learning, including Generalized Linear Models and N-dimensional models.
Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.
Explores linear models, overfitting, and the importance of feature expansion and adding more data to reduce overfitting.
Explores laser fundamentals, noise sources, and beam steering technologies using optical fibers and ultrafast lasers.
Explores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.
