Teaching and learning activities include face-to-face lectures and practice sessions. The instructor presents quantitative methods with the theoretical background along with practical examples and exercises in management science and economics
Contents
Elements of linear algebra and matrix calculus. Recap of mathematical calculus.
Linear programming and elements of non linear programming. Foundations of static optimization.
Difference equation and qualitative analysis of discrete dynamical systems. Principles of dynamic programming.
Notions of random variables and stochastic processes: discrete Markov chains.
Math tools for decision making models: graph theory and network analysis, risk analysis and decision trees.
