Optimization for Machine Learning

An advanced course on the optimization algorithms that make training large ML models on large datasets feasible. Students analyze convergence of first-order methods such as stochastic gradient descent, focus on the non-convex losses common in deep learning, and practice reading, designing, and implementing optimization algorithms from the research literature.