Created as part of the Master of Computer Science degree, and is one of six core units in the Artificial Intelligence specialisation.

On successful completion of this unit, you should be able to:

1. model a discrete optimisation problem using a mix of basic and more advanced modelling techniques in a high level modelling language;
2. interpret and explain models written by others;
3. explain how models are mapped to solver-level input;
4. identify and fix errors in models;
5. evaluate the limitations, appropriateness and benefits of different modelling patterns for common problem classes;
6. evaluate and improve the efficiency of models by applying different model transformations.

020307

This unit introduces the fundamentals of modelling for discrete optimisation, focusing on how to rigorously express a discrete optimisation problem in a manner that can be solved. Some topics covered will include decision variables, basic constraints, modelling with sets, modelling with functions, multiple modelling viewpoints, debugging discrete optimisation models as well as the fundamental algorithms for solving discrete optimization problems.

Online

In-semester assessment: 100%

FIT

ITO5047, ITO5136, ITO5163