Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.
Discusses Stochastic Gradient Descent and its application in non-convex optimization, focusing on convergence rates and challenges in machine learning.
Covers the basics of optimization, including historical perspectives, mathematical formulations, and practical applications in decision-making problems.
