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Parametric Regressions: Linear Regression

Explores parametric regressions, emphasizing linear regression's simplicity and complexity trade-offs between parametric and non-parametric models.

Algorithms for Composite Optimization

Explores algorithms for composite optimization, including proximal operators and gradient methods, with examples and theoretical bounds.

Gradient Noise: Smooth and Nonsmooth Risks

Explores the impact of gradient noise on optimization algorithms, focusing on smooth and nonsmooth risk functions and the derivation of gradient noise moments.

Dynamic Programming: Optimal Control

Explores Dynamic Programming for optimal control, covering machine replacement, Markov chains, control policies, and linear quadratic problems.

Stochastic Optimization: Portfolio Optimization

Explores stochastic optimization in portfolio management, emphasizing decision criteria for uncertain objectives and the concept of conditional value-at-risk.

Bayesian Decision Theory: Utility, Risk, and Classification

Covers Bayesian decision theory, cost functions, classification, and decision rules.

Mathematics of Data: Data, Models, and Optimization

Covers the role of models and data in statistical learning and optimization formulations, with examples of classification, regression, and density estimation problems.

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