STEM / applied
Optimization for CS · Graduate · CS / Programming
Topics
ML and engineering applications
- Regularized regression as convex programs
- ADMM for distributed optimization (intro)
- Hyperparameter optimization and Bayesian opt (survey)
- Optimal control and model predictive control (intro)
- Solver tooling: CVXPY, Gurobi, MOSEK (survey)
Large-scale methods
- Coordinate descent and proximal algorithms
- Variance reduction methods (SVRG-style intro)
- Low-rank matrix completion applications
- GPU acceleration for optimization workloads
- Case studies from ML, robotics, and OR
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.