Courses

Content: 1. Introduction – Motivation, Data-driven versus Model-driven appraoch, importance of data-driven optimization; overview of optimization problems arising in machine learning applications; 2. Preiminaries – linear algebra; convex sets convex functions; gradient, sub-gradient, hessian matrix; 3. Programming basics (Python, R, Matlab); data loading and preprocessing; 4. Unconstrained optimization for machine learning: regularization-meaning and relevance; regression […]

Content: Linear Optimization: Theory of linear programming, degree of freedom, feasible region, graphical description/solution, Simplex method, mixing problem, optimal production planning Nonlinear Optimization: Convexity analysis, problems without uand with constraints, optimality condition, the gradient-, Newton-, Quasi-Newton-methods, KKT conditions, sequential quadratic programming (SQP) methods, active-set method, approximation of the Hessian matrix, application in optimal design of […]

Content: Modeling of linear processes: Modeling with differential equations Linearization of nonlinear systems State space model Laplace transformation: Laplace transformation of typical functions Properties of Laplace transformation Transfer function Analysis of control systems in time domain: Dynamics of different plants Responses due to typical input signals Functions of typical controller Stability analysis