Optimization for Machine Learning: Non-convex

This lecture covers non-convex optimization in the context of machine learning, focusing on gradient descent on smooth functions, trajectory analysis, linear models with multiple outputs, and minimizing least squares error. The instructor explains the theoretical foundations and practical implications of these concepts, providing insights into the convergence properties and challenges of optimization algorithms.