Education and Training

Teaching in computer science, applied mathematics, and artificial intelligence at ESATIC and through guest lectures at other institutions of higher education.

Algorithms and data structures

Variables, control structures, arrays, lists, recursion, data structures, and algorithm analysis.

Complexity of problems and algorithms

Asymptotic notations, algorithm analysis, P, NP, and NP-completeness.

Optimization

Optimization methods, model formulation, solution finding, and decision support.

Linear programming

Modeling linear problems, the simplex method, duality, and sensitivity analysis.

Graph theory

Representation of graphs, paths, spanning trees, shortest paths, and flows.

Programming and Python

Structured programming, functions, data structures, and algorithm implementation in Python.

Artificial intelligence and data science

Introduction to machine learning methods, data preparation, and predictive models.