Lectures related to Box-Cox Transform

Explores the Box-Cox transform for modeling non-linear relationships between variables.

Covers optimization techniques in machine learning, focusing on convexity and its implications for efficient problem-solving.

Covers the process of parameter estimation for spike times in neuron models.

Introduces the key concepts of convexity and its applications in different fields.

Introduces convex sets and functions, discussing minimizers, optimality conditions, and characterizations, along with examples and key inequalities.

Explores channel capacity and convex functions in information theory, emphasizing the importance of convexity.

Explores subgradients in convex functions, emphasizing non-differentiable yet convex scenarios and properties of subdifferentials.

Introduces optimization basics, covering logistic regression, derivatives, convex functions, gradient descent, and second-order methods.

Covers basics on optimization, including norms, Lipschitz continuity, and convexity concepts.

Covers the modelling and optimization of energy systems, focusing on solving optimization problems with constraints and variables.

Discusses the theorems on convex functions and provides examples for better understanding.

Explores the Rockafellar Theorem in optimal transport, focusing on c-cyclical monotonicity and convex functions.

Covers the properties and operations of convex functions.

Explores convex functions, affine transformations, pointwise maximum, minimization, Schur's Lemma, and relative entropy in mathematical optimization.

Covers the discussion of local extrema, concavity, convexity, and inflection points in functions.

Covers the concept of convex functions and their applications in optimization problems.

Introduces convex optimization, focusing on the importance of convexity in algorithms and optimization problems.

Explores convex functions, global minima, and their relationship with differentiability.

Covers geodesically convex optimization on Riemannian manifolds, exploring convexity properties and minimization relationships.

Covers linear models for classification, focusing on logistic regression and support vector machines.