Optimization Methods in Machine Learning

3 Credits

Machine learning models and optimization methods that are used to apply these models in practice. Convex models. Gradient and subgradient methods and their stochastic counterparts. Limits and errors of learning, noise reduction, and nonconvex models. Other techniques and algorithms include acceleration, coordinate descent, alternating-direction methods, first-order constrained convex optimization methods, and second-order methods.