It is assumed that students have knowledge of computer programming, linear algebra, linear optimization and integer optimization.

The objectives of this course are that students attain the following competences: 1) Using advanced modeling and optimization software for setting up and solving large-scale data-driven optimization problems; 2) formulating nonlinear optimization models with or without constraints in diverse application areas, and analyzing them by applying optimality conditions. 3) applying optimization methods and software for the formulation and computational solution of machine learning models; 4) formulating and solving models of optimization under uncertainty, in particular stochastic dynamic optimization models.

1. Advanced optimization modeling software 1.1. Optimization and data science 1.2. Algebraic modeling languages 2. Nonlinear optimization 2.1. Introduction 2.2. Unconstrained optimization 2.3. Equality-constrained optimization 2.4. Inequality-constrained optimization 3. Optimization and machine learning 3.1. Introduction 3.2. Support vector machines 3.3. Logistic regression 3.4. Optimization and neural networks 3.5. Classification trees: classical and optimal 4. Optimization under uncertainty 4.1. Introduction 4.2. Simulation 4.3. Dynamic stochastic optimization

Theoretical-practical classes with web-based supporting material. Computational sessions with Python-based optimization software. The teaching methodology has an eminently practical approach, being based on the formulation and solution of optimization models from diverse application areas.