These notes are from some of my classes and reading in grad school.

- Robust and stochastic optimization

— robust, chance-constrained and sampled convex programming - Convex analysis

— convex sets and functions

— Fenchel, conic and Lagrangian duality

— algorithms for nonsmooth optimization

— variational analysis and the KKT conditions - Linear programming

— geometry and duality

— simplex, ellipsoid and interior point methods

— extension to conic programming - Monte Carlo simulation

— sample sizes and confidence intervals

— random number generation

— variance reduction

— gradient estimation - Model-based estimation

— batch least-squares estimation

— the Kalman filter and square-root techniques

— nonlinear filtering (EKF, UKF, and particle filters) - Matrix algebra review

— norms, factorizations, derivatives and eigenstuff - Probability and statistics basics

— distributions, moments, correlation and covariance, transformations and limit theorems

— maximum likelihood and Bayesian estimation, hypothesis testing