Explores the Box-Cox transform for investigating nonlinear specifications in models using a parameter lambda.
Covers the discussion of local extrema, concavity, convexity, and inflection points in functions.
Introduces convex optimization, focusing on the importance of convexity in algorithms and optimization problems.
Introduces convex sets and functions, discussing minimizers, optimality conditions, and characterizations, along with examples and key inequalities.
Introduces optimization basics, covering logistic regression, derivatives, convex functions, gradient descent, and second-order methods.
