Lectures related to Gradient Descent: Principles and Applications

Covers optimization on manifolds, focusing on smooth manifolds and functions, and the process of gradient descent.

Discusses optimization techniques, focusing on local and global extrema in functions.

Explores self-concordant functions, their properties, definitions, and optimization in mathematical problems.

Explains necessary conditions for convergence in optimization problems.

Covers the integration of machine learning with scanning probe microscopy for enhanced automation and efficiency in scientific workflows.