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