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Explores implicit bias, gradient descent, stability in optimization algorithms, and generalization bounds in machine learning.

Structures in Non-Convex Optimization

Covers non-convex optimization, deep learning training problems, stochastic gradient descent, adaptive methods, and neural network architectures.

Optimal Control: KKT Conditions

Explores optimal control and KKT conditions for non-linear optimization with constraints.

Stochastic Optimization: Algorithms and Methods

Explores stochastic optimization algorithms and methods for convex problems with smooth and nonsmooth risks.

Optimization Methods in Machine Learning

Explores optimization methods in machine learning, emphasizing gradients, costs, and computational efforts for efficient model training.

Introduction to Optimization

Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.

Optimization Basics: Unconstrained Optimization and Gradient Descent

Covers optimization basics, including unconstrained optimization and gradient descent methods for finding optimal solutions.

Optimality of Convergence Rates: Accelerated/Stochastic Gradient Descent

Covers the optimality of convergence rates in accelerated and stochastic gradient descent methods for non-convex optimization problems.

Introduction to Optimization and Operations Research

Covers fundamental concepts of optimization and operations research, exploring real-world examples and key topics over a semester.

Proximal Gradient Descent: Optimization Techniques in Machine Learning

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.