Knowledge and understanding: Students are able to ...

identify and describe a research or innovation problem (Objective 2.1)
model a real-world problem as a numerical optimisation problem.
correctly define the optimisation problem and its objectives for numerically finding optimised solutions and feasible search alternatives.
explain and differentiate the basics and concepts of Computational Intelligence (CI) - i.e, nature-inspired AI - and other related methods used for numerical optimisation.

Application of knowledge and understanding: Students can ...

create improvements and innovations (Objective 3.2)
analyse the optimisation problem by modelling the problem model and the optimisation method.
create a computational optimisation experiment to produce optimisation results.
apply selected methods of Computational Intelligence to understand and practise selected optimisation techniques.
carry out modelling, simulation and optimisation techniques to solve problems.
apply, develop and implement a software prototype to numerically solve optimisation problems.

Ability to make judgments: Students can ...

evaluate the results of the computational optimisation.
draw conclusions based on the computational optimisation experiment and the computational results.
apply and evaluate optimisation principles and methods to solve problems to address future challenges in business, society and science.

Communication: Students can ...

Self-learning skills: Students can ...

Applied Computational Intelligence